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Silicon photodiodes as the VUV photosensor in gas proportional scintillation counters IEEE Trans Nucl Sci, 47(3), 928 932 (2000) Lopes, J A M, dos Santos, J M F and Conde, C A N A large area avalanche photodiode as the VUV photosensor in gas proportional scintillation counters, Nucl Instrum Methods Phys Res, A454, 421 425 (2000) dos Santos, J M F, Soares, A J V D, Monteiro, C M B, Morgado, R E and Conde, C A N The application of the curved-grid technique to a gas proportional scintillation counter with a small-diameter photomultiplier tube IEEE Trans Nucl Sci, 45(3), 229 233 (1998) Lopes, J A M, dos Santos, J M F, Morgado, R E and Conde, C A N Silicon a xenon gas proportional scintillation counter with a UV-sensitive, large-area avalanche photodiode IEEE Trans Nucl Sci.

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1. Download the last version of WordPress that you need from http://wordpress. org/download/release-archive/. 2. Extract the archive to create a wordpress/ folder. 3. With phpMyAdmin or via the MySQL commandline (over SSH), drop all of the WordPress tables in the database. These usually start with wp_ but could be different based on the value of your $table_prefix variable in wp-config.php.

Figure 7.5 shows the operating principle of a class D power ampli er. The inductor Lo and capacitor Co are resonant at the operating frequency. A class D power

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, 48(3), 312 319 (2001) Borges, F I G M, Santos, F P, dos Santos, J M F, Dias, T H V T, Rachinhas, P J B M and Conde, C A N The performance of a gas proportional scintillation counter for X-ray spectrometry in the 01 3 keV range IRRMA-V 5th International Topical Meeting on Industrial Radiation and Radioisotope Measurement Applications submitted Sim es, P C P S, dos Santos, J M F, and Conde, o C A N Digital risetime discrimination for peak enhancement analysis X-Ray Spectrom, 26, 182 188 (1997)..

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he foundation for this book, the Information Architecture Principle (introduced in 1), puts into words the reason why there must be a solid recovery plan: Information is an organizational asset, and, according to its value and scope, must be organized, inventoried, secured, and made readily available in a usable format for daily operations and analysis by individuals, groups, and processes, both today and in the future. It goes without writing that for information to be readily available . . . both today and in the future, regardless of hardware failure, catastrophes, or accidental deletion, there has to be a plan B. Obviously, this is an imperfect world and bad things do happen to good people. Since you re bothering to read this chapter, I ll be honest and agree that doing backups isn t very exciting. In some jobs excitement means trouble, and this is one of them. To a good DBA, being prepared for the worst means having a sound recovery plan that has been tested more than once. Consistent with the flexibility found in other areas of SQL Server, there are multiple ways to perform a backup, each suited to a different purpose. SQL Server offers three recovery models, which help organize the backup options and simplify database administration. This chapter discusses the concepts that support the recovery effort, which entail both backup and restoration. It seems foolish to study backup without also learning about how restoration completes the recovery.

instances (e.g. IT and knowledge management) and which do not (load management and knowledge management). Also it is clear that IT plays a part in both types of management. The relative sizes of the classes are clearly visible, e.g. there are a lot more pages written about load management than knowledge management. Another interesting aspect of the visualization is that proximity in the map is related to semantic proximity. This is a consequence of the graph layout algorithm. Classes are semantically close if they share many instances. Indeed, the more instances two classes share, the closer they are in the visualization. Instances are semantically close if they belong to the same class(es). Indeed, instances that have the same class memberships are explicitly clustered. Figure 9.5 shows a more extensive example of a cluster map, displaying the cluster model of four key terms within the EnerSearch domain. Here, the clusters are displayed as cylinders, abstracting from the individual instances they contain. This allows the visualization to scale to larger amounts of instances. Furthermore, the edges connecting the same class to various clusters all have the same colour, so that the distribution of its instances over the map is easier to spot. The static version of the cluster map (i.e. the image itself) already contains a lot of information that can be useful for analysing the data set. The classes and their hierarchical relationships provide an understanding of the domain of the

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