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Too often, the implementations of CIM have created a compatibility nightmare in today s multivendor factory-floor environments. Too many end users have been forced to discard previous automation investments and/or spend huge sums on new equipment, hardware, software, and networks in order to effectively link together data from distinctly dissimilar sources. The expense of compatible equipment and the associated labor cost for elaborate networking are often prohibitive. The claims of CIM open systems are often misleading. This is largely due to proprietary concerns, a limited-access database, and operating system compatibility restrictions. The systems fail to provide the transparent integration of process data and plant business information that makes CIM work. In order to solve this problem, it is necessary to establish a clearly defined automation program. A common approach is to limit the problem description to a workable scope, eliminating those features not amenable to consideration. The problem is examined in terms of a simpler workable model. A solution can then be based on model predictions. The danger associated with this strategy is obvious: If the simplified model is not a good approximation of the actual problem, the solution will be inappropriate and may even worsen the problem. Robust automation programs can be a valuable asset in deciding how to solve production issues. Advances in sensor technology have provided the means to make rapid large-scale improvements in problem solving and have contributed in essential ways to today s manufacturing technology. The infrastructure of an automation program must be closely linked with the use and implementation of sensors and control systems, within the framework of the organization. The problem becomes more difficult whenever it is extended to include the organizational setting. Organization theory is based on a fragmented and partially developed body of knowledge, and can provide only limited guidance in the formation of problem models. Managers commonly use their experience and instinct in dealing with complex production problems that include organizational aspects. As a result, creating a competitive manufacturing enterprise one involving advanced automation technology utilizing sensors and control systems and organizational aspects is a task that requires an understanding of both how to establish an automation program and how to integrate it with a dynamic organization. In order to meet the goals of integrated sensory and control systems, an automated manufacturing system must be built from compatible and intelligent subsystems. Ideally, a manufacturing system should be computer-controlled and should communicate with controllers and materials-handling systems at higher levels of the hierarchy, as shown in Fig. 1.3.
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Work station controller CPU and memory Disk controller Serial communications Parallel I/O Time and date CRT terminal Auxiliary sensors Safety sensors Tool setting Adaptive control Controller Controller Tray transport and buffering Swing clamp Vise Sense switch Automated fixturing Work station system software Local data base Disk storage
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The sequencing of DNA molecules began in the 1970s with development of the Maxam-Gilbert method, and later the Sanger method. Originally developed by Frederick Sanger in 1975, most DNA sequencing that occurs in medical and research laboratories today is performed using sequencers employing variations of the Sanger method. Termed the chain-termination method, it involves a reaction where chain-terminator nucleotides are labeled with fluorescent dyes, combined with fragmented DNA, DNA sequencing primers, and DNA polymerase. Each nucleotide in the DNA sequence is labeled with a different dye color, and a chromatogram is produced, with each color representing a different letter in the DNA code A, T, C, or G. Advances in sequencing technology and computer programming enabled relatively fast and cost-efficient DNA sequencing. However, sequencing of entire genomes of organisms was difficult and time consuming. At the time the Human Genome Project was officially started in 1990, it was thought that sequencing the human genome would take 15 years. The sequence was released in 2003, although some gaps still exist. The estimated project cost for the entire Human Genome Project was around $3 billion, although this figure represents a wide range of scientific activities that went into the project, not exclusively genome sequencing. Optimally, current sequencers are able to sequence approximately 2.8 million base pairs
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