Strategic Marketing planning in the British pharmaceutical industry is discussed, with the key areas of marketing personnel, planning procedures and associated analytical tools being focused upon. The responses of a number of companies to whom detailed questionnaires were sent, are shown in tables. These detail, for example, who is responsible, in a particular company for strategic planning, issues, and use of analytical tools. The impression gained from these responses show an extremely marketing-oriented industry.
We report on a measurement of the mass of the Z boson, its total width, and its partial decay widths into hadrons and leptons. On the basis of 25 801 hadronic decays and 1999 decays into electrons, muons or taus, selected over eleven energy points between 88.28 GeV and 95.04 GeV, we obtain from a combined fit to hadrons and leptons a mass of = 91.154 ± 0.021 (exp) ± 0.030 (LEP) GeV, and a total width of = 2.536 ± 0.045 GeV. The errors on have been separated into the experimental error and the uncertainty due to the LEP beam energy. The measured leptonic partial widths are = 81.2 ± 2.6 MeV, = 82.6 ± 5.8 MeV, and = 85.7 ± 7.1 MeV, consistent with lepton universality. From a fit assuming lepton universality we obtain ℓ = 81.9 ± 2.0 MeV. The hadronic partial width is = 1838 ± 46 MeV. From the measured total and partial widths a model independent value for the invisible width is calculated to be = 453 ± 44 MeV. The errors quoted include both the statistical and the systematic uncertainties.
N-fold inspection uses traditional inspections of the user requirements document (URD) but replicates the inspection activities using N independent teams. A pilot study was conducted to explore the usefulness of N-fold inspection during requirements analysis. A comparison of N-fold inspection with other development techniques reveals that N-fold inspection is a cost-effective method for finding faults in the URD and may be a valid technique in the development of mission-critical software systems.
Economic analysis of the hardware characteristics of the personal computer segment of the microcomputer market indicates that the variables that most affect cost are the bundle of attributes offered by the micros. Interestingly, certain technological characteristics found to be important explanatory variables for cost in larger computers were not found to be significant for micros.
Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see ), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit . A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc. We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem. This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems. Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.