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Page 90 In addition to client consultation at an earlier stage in the system implementation process, it remains of ultimate importance to determine whether the clients for whom the project has been initiated will accept it Client acceptance refers to the final stage in the implementation process, at which time the overall efficacy of the project is to be determined Too often project managers make the mistake f believing that if they handle the other stages of the implementation process well, the client (either internal or external to the organization) will accept the resulting system Client acceptance is a stage in project implementation that must be managed like any other Some researchers have even proposed the use of intermediaries to act as a liaison between the designer, or implementation team, and the project s potential users to aid in client acceptance9 Examples of statements referring to client acceptance would include Potential clients have been contacted about the usefulness of the project, and Adequate advanced preparation has been done to determine how best to sell the project to clients The eighth factor to be considered is that of monitoring and feedback Monitoring and feedback is a project control process by which, at each stage of the project implementation, key personnel receive feedback on how the project is comparing to initial projections Making allowances for adequate monitoring and feedback mechanisms gives the project manager the ability to anticipate problems, to oversee corrective measures, and to ensure that no deficiencies are overlooked Project managers need to emphasize the importance of constant monitoring and fine-tuning the implementation For our model, monitoring and feedback refers not only to project schedule and budget, but also to monitoring performance of members of the project team Sample statements for the project manager to consider under the monitoring and feedback factor include When the budget or schedule requires revision, I solicit input from the project team, and All members of the project team know if I am satisfied/dissatisfied with their work .

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Brownian motion A typical machine is either active, or inactive; the durations of activity and inactivity are independent An in nity of machines is then considered At the time tT , we consider all the active machines more exactly, the uctuations around the average number of active machines at time tT Then, we renormalize in T to obtain the law of this phenomenon The set of all durations of active machines at a given time is distributed in a rather similar way to that of the balls of the distribution model of non-renormalized mass which we will study later The reader is also directed to 12 The goal of this chapter is to present a set of stochastic processes which have partial self-similarity and stationarity properties Unfortunately, we cannot claim to present an exhaustive study moreover, the available space prohibits it We had to make choices We referred to challenge the reader by asking him or her questions whose answers appeared to us as surprising The intention is to show that the concept of scale invariance remains, to a great extent, misunderstood We thus hope, with what has been said before, centered on physics, and which we will present, to have opened paths which other researchers will perhaps follow Trees will be used here as the leading path to scale invariance It seemed to us that such a simple geometric structure, with such great exibility, is well-adapted to the study of self-similarity In order to become familiarized with trees and invariance by dilation and translation, we begin our presentation with a study of purely geometric scaling, where trees and spaces are mixed From this, we present random or non-random fractals with scale invariance This leads us to the model of mass distribution This provides a convenient means to generate a quantity of processes, stochastic or not, with scale and translation invariance It is remarkable that, through a suitable wavelet decomposition, all the self-similar stochastic processes with stationary increments relate to a layer-type model, except perhaps for Takenaka s process These models of scale invariance enable us to question the difference between two concepts of equal importance: long-range correlation and sample path regularity From examples, we show that these two concepts are independent This chapter consists of four sections The rst is mostly an introduction The second clari es the Gaussian case, a quasi-solved problem The third section turns to some non-Gaussian cases, mostly that of stable processes The last section studies correlation and regularity from defective examples Generally, certain technical dif culties, sometimes even major ones, are overlooked Consequently, the results may give the impression of lack of rigor, while returning to the original work is a necessity.

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