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A hard drive consists of three major components: A set of platters. This set of platters is the main component, and the platters are made of either an aluminum alloy or glass/ceramic, and coated with a magnetic medium. Modern platters are double-sided, and all hard drives have more than one. In modern hard drives the platters are spun at 5,400 RPM (revolutions per minute) or faster. It is onto these platters that the data is written. Drive heads. The reading and writing of the data on the platters is carried out by the drive heads. Each platter surface has a head that does the reading and writing. These heads are fitted onto an actuator arm and driven by a motor. Motor. Finally, there s a motor that spins the platters around so that they can be written to and read from. The entire hard drive is then encased in a metal box, and the connectors for it are all at one end. A modern hard drive is a magnificent feat of engineering. With the platters spinning at full speed, the distance between the read/write heads and the platters is around 50 nanometers (50 millionths of a meter). Compare this to a human hair, which is about 2,000 times thicker than this. The inside of a hard drive has to be free of dust and debris (they are assembled in a clean-room environment) because given the speed of the rotating platters and the small tolerances involved, even the smallest particle of dust getting trapped between the head and the platters could cause tremendous damage to the drive, damaging both the head and the surface of the platters.

In the half-duplex mode of operation, a network node does not transmit unless the network medium is silent. It then transmits and while doing so attempts

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n this chapter, you learn to create all types of surfaces, also called meshes. Surfaces have a great advantage over 3D wireframe models because you can hide back surfaces and create shaded images for easier visualization of your models. Surfaces also enable you to create unusual shapes, such as for topological maps or free-form objects.

Because AutoCAD doesn t retain source objects when you use the UNION, SUBTRACT, or INTERSECT commands, it would be nice to have a way to modify the original objects separately. Solid history does just that.

ELECTRONIC PATIENT RECORD (ePR)

Seismic data from multiple sources and receivers need to be combined based on the traveltimes of the sound waves into the subsurface to form subsurface images. This process is known as Kirchhoff migration [13] and is routinely implemented in the data domain of the observations (space and time) using parallel computer architectures, particularly PC clusters [17]. However, the same data can rst be transformed into a new spectral domain (plane wave component and vertical delay time) using a parallel algorithm as the eld data are read, which regularizes the data to be processed. Once transformed, the imaging can again proceed in parallel since each plane wave component can be imaged independently. Computational resources can be dynamically adjusted as the data are transformed based on the number of plane wave components that will contribute to the nal image. Ef ciencies over conventional Kirchhoff migration algorithms range from 10 100 based on the reduced input data volume since the number of plane wave components employed will be less than the recorded data volume.

model algorithm for each operation in the manufacturing process would be generated from a subset of the control space spanned by the material qualities and processing parameters which impact that operation. Each unit operation control model seeks to adjust process parameters in a timely manner in response to changes in raw material (feedforward) or nished-product (feedback) quality. In other words, control the process and validate the controls. The mathematical linkage of the design space, process, and control models enables continuous optimization of product quality by seeking the optimal point within the control space. As the level of process understanding increases or as processing conditions evolve, factors might be added or removed from the design space and the process and control models updated. Furthermore, by considering other factors such as yield, ef ciency, or C/T as a function of the variables spanned by the process design space, the process might be co-optimized for quality and pro tability. It is likely that many pharmaceutical manufacturing operations are not understood in a way that product quality variance can be fully described in functional form (e.g., transfer functions); attaining such a level of manufacturing knowledge should be a goal for the industry. Using functional representations of process understanding as the basis set for a process design space, rather than historical performance, offers many operational advantages:

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