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It was shown that most existing geocasting schemes do not guarantee delivery to all nodes inside a region, the main reasons being either the partitioning of the network inside the region, or applying greedy routing instead of one that guarantees delivery Yu, Govindan, and Estrin [22] considered a geocasting variant of the datagathering problem They describe the geographic and energy-aware routing (GEAR) algorithm, which uses energy-aware neighbor selection to route the packet toward the target region, and recursive geographic forwarding, or restricted blind ooding algorithm, to disseminate the packet inside the destination region Recursive forwarding applies GEAR to send messages to four subregions in the geocast region, which repeats until the region has a single node inside it.

obesity in the Pima Indians of rural Mexico is less than half that of their Arizona counterparts Although the two groups share the same genetic makeup, they differ dramatically in their lifestyles The Pimas in rural Mexico consume a diet with less animal fat and more complex carbohydrates, and they expend a greater amount of energy in physical labor than do their cousins in Arizona (Ravussin, Valencia, Esparza, Bennett, & Schultz, 1994) Thus, environments that foster appropriate food consumption and energy expenditure can limit the development of obesity even in the presence of a strong genetic predisposition Alternatively, environments that offer unlimited access to high-calorie foods and simultaneously support low levels of physical activity can promote obesity even in the absence of a speci c genetic predisposition.

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, effects of anabolic steroids, consequences of eating disorders), improved mood, and fewer intentions to engage in unhealthy behaviors in the future (eg, vomit to lose weight) than athletes in the control condition These two programs suggest that primary prevention can be effective with high school and college-age student athletes, particularly when the format is interactive and the content designed to address the unique needs of this population A recent meta-analysis of the general eating disorder prevention research (Stice & Shaw, 2004) suggests that, overall, prevention programs have small to medium effects in reducing a range of eating disorder risk factors, including body dissatisfaction, negative affect, internalization, eating pathology, and actual body mass More specifically, by examining moderating influences, Stice and Shaw identified the key factors that determined a program s effectiveness.

As several authors (Hill & Peters, 1998; Poston & Foreyt, 1999) have noted, the human gene pool has not changed in the past quarter century Consequently, the increased prevalence of obesity in the United States and other Western countries must be due to the in uence of environmental factors on energy consumption and/or energy expenditure Are Americans eating more food and taking in more calories Research on the trends in energy intake has been inconclusive (Ernst, Sempos, Briefel, & Clark, 1997; Nestle & Woteki, 1999) Some surveys (eg, Norris et al, 1997) show that energy intake has been declining, whereas others (eg, Centers for Disease Control and Prevention, 1994) suggest that energy intake has been rising Because surveys of selfreported food consumption are susceptible to response biases, alternative methods of gauging population trend in energy intake are worth examining.

Blind ooding does not guarantee delivery to all sensors inside the region, because of possible partition inside the region (but connectivity outside it), and can be replaced by a more intelligent scheme (see 11 in this book) The GEAR algorithm selects a forwarding neighbor (among those that are closer to the destination), which minimizes a linear combination of their distance to the destination and the energy they already spent This is almost equivalent to the cost-aware localized scheme by Stojmenovic and Lin [7], originally proposed in 1998 (described in detail in 12) Yu, Govindan, and Estrin [22] also claim that GEAR can avoid holes by applying a learning A algorithm-based approach, without presenting details To avoid holes, one can use, for example, the depth- rst search (DFS) approach [23] This approach requires memorizing past traf c at nodes.

First, successful programs targeted at-risk populations, such as women with high levels of body dissatisfaction, instead of simply including all potential participants Second, programs that were interactive, as opposed to simply didactic, had the strongest effects By giving participants the opportunities to interact with one another, learn new skills and ideas, and practice new behaviors, changes in attitudes and behaviors occurred Third, the interventions were most effective with participants age 15 and over, which is the time period of greatest risk for disordered eating pathology and when young women may be most motivated for change due to the psychological distress they are experiencing Fourth, multiversus single-session programs were more effective in promoting changes among participants These effects are like-.

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