3,769 research outputs found
Applying performance measures to support informed decision making at an operational level
Performance Measurement Systems (PMS) have commonly been applied to evaluate and reward performances at managerial levels, especially in the context of supply chain management. However, evidence suggests that the effective use of PMS can also positively influence the behaviour and improve performance at an operational level. The purpose of the study described in this paper is to develop a conceptual framework that adopts performance measures for ex-ante decision-making at an operational level within the supply chain. A case study at Coca-Cola Enterprises has been carried out and as a result, a conceptual framework of the PMS has been developed
Influence of proton bunch parameters on a proton-driven plasma wakefield acceleration experiment
We use particle-in-cell (PIC) simulations to study the effects of variations
of the incoming 400 GeV proton bunch parameters on the amplitude and phase of
the wakefields resulting from a seeded self-modulation (SSM) process. We find
that these effects are largest during the growth of the SSM, i.e. over the
first five to six meters of plasma with an electron density of cm. However, for variations of any single parameter by
5%, effects after the SSM saturation point are small. In particular, the
phase variations correspond to much less than a quarter wakefield period,
making deterministic injection of electrons (or positrons) into the
accelerating and focusing phase of the wakefields in principle possible. We use
the wakefields from the simulations and a simple test electron model to
estimate the same effects on the maximum final energies of electrons injected
along the plasma, which are found to be below the initial variations of
5%. This analysis includes the dephasing of the electrons with respect to
the wakefields that is expected during the growth of the SSM. Based on a PIC
simulation, we also determine the injection position along the bunch and along
the plasma leading to the largest energy gain. For the parameters taken here
(ratio of peak beam density to plasma density ), we
find that the optimum position along the proton bunch is at , and that the optimal range for injection along the plasma (for
a highest final energy of 1.6 GeV after 10 m) is 5-6 m.Comment: 9 pages, 12 figure
Extreme values for Benedicks-Carleson quadratic maps
We consider the quadratic family of maps given by with
, where is a Benedicks-Carleson parameter. For each of these
chaotic dynamical systems we study the extreme value distribution of the
stationary stochastic processes , given by , for
every integer , where each random variable is distributed
according to the unique absolutely continuous, invariant probability of .
Using techniques developed by Benedicks and Carleson, we show that the limiting
distribution of is the same as that which would
apply if the sequence was independent and identically
distributed. This result allows us to conclude that the asymptotic distribution
of is of Type III (Weibull).Comment: 18 page
Extreme Value Laws in Dynamical Systems for Non-smooth Observations
We prove the equivalence between the existence of a non-trivial hitting time
statistics law and Extreme Value Laws in the case of dynamical systems with
measures which are not absolutely continuous with respect to Lebesgue. This is
a counterpart to the result of the authors in the absolutely continuous case.
Moreover, we prove an equivalent result for returns to dynamically defined
cylinders. This allows us to show that we have Extreme Value Laws for various
dynamical systems with equilibrium states with good mixing properties. In order
to achieve these goals we tailor our observables to the form of the measure at
hand
Extreme Value Laws for sequences of intermittent maps
We study non-stationary stochastic processes arising from sequential
dynamical systems built on maps with a neutral fixed points and prove the
existence of Extreme Value Laws for such processes. We use an approach
developed in \cite{FFV16}, where we generalised the theory of extreme values
for non-stationary stochastic processes, mostly by weakening the uniform mixing
condition that was previously used in this setting. The present work is an
extension of our previous results for concatenations of uniformly expanding
maps obtained in \cite{FFV16}.Comment: To appear in Proceedings of the American Mathematical Society. arXiv
admin note: substantial text overlap with arXiv:1510.0435
Extreme Value Laws for non stationary processes generated by sequential and random dynamical systems
We develop and generalize the theory of extreme value for non-stationary
stochastic processes, mostly by weakening the uniform mixing condition that was
previously used in this setting. We apply our results to non-autonomous
dynamical systems, in particular to {\em sequential dynamical systems}, given
by uniformly expanding maps, and to a few classes of random dynamical systems.
Some examples are presented and worked out in detail
Speed of convergence for laws of rare events and escape rates
We obtain error terms on the rate of convergence to Extreme Value Laws for a
general class of weakly dependent stochastic processes. The dependence of the
error terms on the `time' and `length' scales is very explicit. Specialising to
data derived from a class of dynamical systems we find even more detailed error
terms, one application of which is to consider escape rates through small holes
in these systems
Complete convergence and records for dynamically generated stochastic processes
We consider empirical multi-dimensional Rare Events Point Processes that keep
track both of the time occurrence of extremal observations and of their
severity, for stochastic processes arising from a dynamical system, by
evaluating a given potential along its orbits. This is done both in the absence
and presence of clustering. A new formula for the piling of points on the
vertical direction of bi-dimensional limiting point processes, in the presence
of clustering, is given, which is then generalised for higher dimensions. The
limiting multi-dimensional processes are computed for systems with sufficiently
fast decay of correlations. The complete convergence results are used to study
the effect of clustering on the convergence of extremal processes, record time
and record values point processes. An example where the clustering prevents the
convergence of the record times point process is given
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