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The default implementation for hashCode() is to use the System.identityHashcode() method to obtain its hashcode. This method typically returns a hash related to the memory address where the object is allocated. The end effect of using the default implementation of equals() and hashCode() is that two objects are equal only if they are the exact same Java object. For some Java applications, this is adequate; for the PIX system, however, it is a problem. Custom equals() and hashCode() methods are required because objects such as PixUser can be stored to and fetched from a relational database. (The next chapter shows how this is done.) If you compare an instance of PixUser created by means of its constructor against another instance fetched from the database, you want to consider them equal if all their data members have the same value. A PixUser is considered equal by this application to another if he or she has the same e-mail, first name, last name, user name, and password. You do not need them to be the same Java object, and indeed they might not be, so the default equals() and hashCode() are not suitable. In the next Try It Out section, you implement custom identity methods: equals() and hashCode() for the PixUser POJO.

This is not only convenient for the new system administrator or someone used to using graphical tools for system configuration, but also it conveniently hides the internal details of the underlying files used Once you settle on a specific Linux distribution, you ll want to become one with the files that it uses for network configuration, but the casual or multidistribution Linux user can save a lot of time and trouble by simply using graphical tools for Linux network configuration and administration More general network configuration information, such as the addresses of DNS name servers and the sequence in which your system looks in different locations for host-to-IP address mappings, is specified in the same text-format configuration files on all Linux systems The files that contain critical pieces of this information are the following:.

csc /nologo /out:AffiliateManagementServiceImpl.dll /target:library AffiliateManagementServiceImpl.cs

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Type B A network connection with acceptable residual error rate but an unacceptable rate of signal failures. Type C A network RANDOM BONUS DEFINITION connection with an unacceptable residual error promiscuous mode A mode of operation rate for the user of the of a network interface in which it receives (or attempts to receive) all traf c, regardless transport service.

Information used in the assessment process may include key project documents, such as the project Work Breakdown Structure (WBS), project execution strategy, project charter, cost and schedule assumptions, scope definitions, engineering designs and studies, economic analyses and any other relevant documentation about the project and its purpose. Other information such as historical data, theoretical analysis, empirical data and analysis, informed opinions of experts and the concerns of stakeholders may also be useful.

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This make it possible for the network administrator to collect measurements for consistent service performance monitoring across (E)GPRS network domains, as explained in Section 9.3.1, or for gathering performance metrics according to speci c needs. In practice, only a subset of the above attributes may be required. For example, in the following sections only the precedence class attribute, denoted by p, is used. 9.3.2.2 Integrity monitoring

Example 8 I n this example, the joint CPE and channel estimator is applied to an OFDM system with parameters listed in Table 3.1. The pilot pattern shown in Figure 3.1 7 is assumed. The UMTS channel (COST2OTTU) with different Doppler frequencies is used. The Doppler frequency is fixed at FdT, = 0.05. We choose the phase noise parameters as suggested a [30] : a = 6 , c = 10.5, f1 = n l k H z , and f 2 = 1 0 k ~ ~ . MSE of composite channel estimates is used as the The performance measure for comparing the following 3 different schemes in rapid dispersive fading channels with phase noise:

7.4.6 7.4.7 7.4.8

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Overall, both body fat and lean tissue may be regulated within genetically determined boundaries by a variety of external and internal factors (Speakman, 2004). External factors range from the availability and energy density of food and (in humans) opportunities for physical exercise, to day length and other cues that drive feeding behaviour in animals preparing for hibernation. Internal modulatory factors include operation of the reproductive axis an activity that is essential for survival of the species, and which in turn depends critically on the individual animal having adequate nutrition and energy stores (Asarian and Geary, 2006).

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