Military objectives in cyber warfare

This Chapter discusses the possible problems arising from the application of the principle of distinction under the law of armed conflict to cyber attacks. It first identifies when cyber attacks qualify as ‘attacks’ under the law of armed conflict and then examines the two elements of the definition of ‘military objective’ contained in Article 52(2) of the 1977 Protocol I additional to the 1949 Geneva Conventions on the Protection of Victims of War. The Chapter concludes that this definition is flexible enough to apply in the cyber context without significant problems and that none of the challenges that characterize cyber attacks hinders the application of the principle of distinction

An experimental study of paternal behavior in red-winged blackbirds

The effect of brood size and female nesting status on male parental behavior was investigated in red-winged blackbirds Agelaius phoeniceus using brood size manipulation experiments. Male redwings allocated parental effort on the basis of brood size and nestling age. Males began assisting females only at nests with at least three offspring older than three days. Female nesting status had no singificant influence on male parental care. When females were unable to meet a brood's demand for food, males assisted females with nestling feeding. Females did not reduce the amount of food delivered to nestlings when males assisted. The amount of food brought to nestlings by the male was additional to the amount of food provided by the female. Male assistance increased fledgling success. When female provisioning was sufficient to meet a brood's demand for food males did not assist. The value of male parental care varied inversely with the ability of the female to meet nestling food demands. The ability of unassisted females to provide sufficient food and to raise a brood of nestlings successfully appeared to be influenced by resource abundance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46891/1/265_2004_Article_BF00299713.pd

Comparative effects of whey and casein proteins on satiety in overweight and obese individuals: A randomized controlled trial

Background/Objective: Dairy protein seems to reduce appetite by increasing satiety and delaying the return of hunger and subsequently lowering energy intake compared with fat or carbohydrate. The aim of this study was to compare the effect of whey with that of casein proteins on satiety in overweight/obese individuals. Methods/Subjects: This was a randomized, parallel-design 12-week-long study. Seventy subjects with a body mass index between 25 and 40 kg/m2 and aged 18–65 years were randomized into one of three supplement groups: glucose control (n=25), casein (n=20) or whey (n=25) protein. Before commencing the study, at weeks 6 and 12 of the treatment, a Visual Analogue Scale (VAS) was used to measure subjective sensations of appetite before lunch and before dinner. Results: Rating for VAS (mm) at 6 and 12 weeks showed significantly higher satiety in the whey group compared with the casein (P=0.017 and P=0.025, respectively) or control (P=0.024 and P=0.032, respectively) groups when measured before lunch. Similarly, at 6 and 12 weeks, the score for fullness was also significantly higher in the whey group compared with both casein (P=0.038 and P=0.022, respectively) and control (P=0.020 and P=0.030, respectively) groups. However, these short-term effects on satiety from dairy whey proteins did not have any long-term effects on energy intake or body weight over 12 weeks compared with casein. Conclusions: Collectively, whey protein supplementation appears to have a positive and acute postprandial effect on satiety and fullness compared with casein and carbohydrate supplementation in overweight and obese individuals

Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

<p>Abstract</p> <p>Background</p> <p>Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.</p> <p>Methods</p> <p>Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.</p> <p>Results</p> <p>There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.</p> <p>Conclusions</p> <p>These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.</p