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Having content that sucks is more often than not a surprise. You think it's smashing content. Your subordinates all tell you you're right. Just count yourself lucky that you've learned something valuable at a small comparative expense. The other two problems, however, tell you a lot about the vendor you're dealing with. Some email list database merchants collect names however and wherever they can. The worst collect them willy-nilly (which stands for will he or nil he-whether the address owner wants it or not). Any of your messages sent to this hodgepodge list will be met with a higher degree of disdain due to the lack of serious targeting. But even the best email address accumulators may run afoul of the salesperson who is so intent on renting you tens of thousands of names he or she doesn't focus on which lists should and which should not be on the recipient list for your offer. Fortunately, this works against them. At YesMail (www.yesmail.com), they are very careful to track which types of messages are of interest to which categories of subscribers. With more than 25 million people signed up, it's important to keep them all as happy as possible. The novice sales consultant, calculating the commissions in advance, might think that a new chat software release might be of interest to all 3,936,384 subscribers to the Internet category and to all 2,958,777 subscribers to the Computer Software category. The sales consultant

DeMarco s Bang Metric COnstructive COst MOdel (COCOMO)

This approach requires access to con guration parameters such as provisioned link bandwidth, logs and built-in counters from several network elements, and considerable costs, complexity and complications are found in practice where it comes to extraction, gathering and correlation of such heterogeneous data in the format and semantics from different elements, with different software platforms and from different vendors In other words, deploying and maintaining a monitoring infrastructure with the same capillarity of the production network might be very expensive On the other hand, the structure of a 3G mobile network is highly hierarchical and centralized: the whole traf c is concentrated in the CN and there are only a few Gateway General Packet Radio Service (GPRS) Supporting Nodes (GGSN) that connect it to external networks such as the Internet, therefore the whole traf c can be captured at a few monitored links.

Responsible for generating new accounts. Negotiated contracts. Hired and trained new sales associates. Assisted marketing department with special projects.

The aim of this case study is to develop methods to infer the presence of performance bottlenecks in the network from the analysis of the traf c captured at a few centralized monitoring points, without direct access to all network links A possible approach is to observe the Transmission Control Protocol (TCP) behaviour: since TCP is closed-loop controlled, its dynamics and performances are dependent on the state of the whole end-to-end ow path In principle it should be possible to infer the presence of performance bottlenecks by looking at the evolution of the TCP aggregate and/or to individual connections at any point along the path.

where N is the number of electric dipole moments per cubic meter. We can also write an equation of motion for the molecules that form the permanent electric dipole moments by equating the torque on the electric dipole with its moment of inertial times the angular acceleration as

We devised two possible strategies to diagnose the presence of a bottleneck on some network element from the analysis of the traf c aggregate routed through it but observed at a different point: from the analysis of the aggregate traf c rate; from the analysis of TCP performance indicators such as frequency of retransmissions and/or round trip times In this case study we explore both approaches This study is based on several weeks of packet traces collected by passive monitoring the core network links of a large operational 3G network during 2006 In the middle of the measurement period a bottleneck link within.

Summary

Fortunately, Visual Basic contains the appropriate conversion tools. Just use StrConv. It offers a rich set of conversion schemes and allows for easy UNICODE-ANSI-UNICODE roundtrips. In the Icon Picker example, the code first checks whether it s being run on a non-Windows 2000 system. If so, it converts the Visual Basic UNICODE strings to ANSI, feeds them to the APIfunction, and reconverts the result back to UNICODE so Visual Basic can handle it.