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Although the actual prevalence of FAT is relatively unknown, Torstveit and Sundgot-Borgen (2005) reported that 60% of elite Norwegian athletes were at risk for the triad, with lean sport athletes having the highest risk, 69% Anorexia Athletica Anorexia athletica (AA), sometimes referred to as activity anorexia, is a state of reduced energy intake and reduced body mass despite high physical performance (Sudi et al, 2004, p 657) Sundgot-Borgen (1993) specified the following criteria for AA: (a) weight loss greater than 5% of expected body weight, ( b) gastrointestinal complaints, (c) absence of illness or disorder to explain weight loss, (d) restricted caloric intake, and (e) an extreme fear of becom-.

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The erasure channel will affect the transmission by erasing some of the transmitted data packets, but the receiver collects a set of N data packets in order to form a matrix of size K N , such that N = K + E x , where E x is the number of excess packets with respect to K , which can be understood as the redundancy in the transmission using the fountain code The question that now arises is, can the original packets of information be successfully recovered from these N received data packets The receiver is assumed to know the so-called fragment generator matrix G fr , which is a matrix formed in the decoder from the N correctly received data packets, and each column of this fragment matrix has a known value of time index n.

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If the decoder also knows the pseudo-random rule that generated the K -tuple columns of the increasing matrix used by the encoder, then it is possible to recover the original information If N < K , then there is no way of successfully recovering the original information For any non-zero value of the excess E x , there is the possibility of a successful recovery In the case of N = K , the original information can be recovered if an inverse matrix of the fragment generator matrix G fr exists The probability of the existence of such an inverse matrix is determined in [24], and it is equal to 1 2 K 1 2 (K 1) (1 1/8)(1 1/4)(1 1/2).

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16.1 INTRODUCTION Wireless multihop networks, also known as packet radio networks and ad hoc networks, hold great promise for providing easy to use mobile services for many applications, especially military and disaster relief communications. Such networks can provide robust communication, be rapidly deployed, and respond quickly in dynamic environments. However, effectively deploying and utilizing such networks poses many technical challenges. One such challenge is to make effective use of the limited channel bandwidth. In this chapter, we describe one approach to this challenge, namely broadcast scheduling of channel usage by way of TDMA (time division multiple access). Our emphasis is on the fundamental computational and algorithmic issues and results associated with broadcast scheduling. The chapter is organized as follows. In the next section we provide background and terminology on broadcast scheduling and related topics. Section 16.3 examines the computational complexity of broadcast scheduling. Sections 16.4 and 16.5 study approximation algorithms in centralized and distributed domains. Section 16.6 briefly outlines some related results. Finally, Section 16.7 summarizes the chapter and outlines prominent open problems.

ES cells have generated a great deal of interest as a source of cells for tissue engineering. Protocols for growing hES cells are established but will need to be

Once written, the software components of a DDBE need to be deployed across the different computer systems within our environment. These computer systems may run different operating systems and support different DBMS software. In order to deploy a software component on one of these target computer systems, we must compile, link, and install it using the facilities provided by the underlying platform (the facilities satisfying the fundamental DDBE Platform requirements, which we will discuss in Section 14.2). In a well-designed architecture, we can reuse the same source code for several different component implementations and deployments (ideally, across the entire set of target operating systems and DBMSs in our environment). In other words, the design and implementation of the components have been done with portability to different systems in mind, as we will discuss in Section 14.2.1.3. Figure 12.4 depicts an example deployment, where a CP and an AP have been deployed to both a Windows system and a UNIX-based system. In this example, since the CP has been deployed on both systems, users on either system can access the information contained within the other system (from both the Oracle and SQL Server databases collectively).

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