A Randomized Greedy Method for AI Applications Component Placement and Resource Selection in Computing Continua

Author(s):  
Hamta Sedghani ◽  
Federica Filippini ◽  
Danilo Ardagna
2018 ◽  
Vol 100 (1) ◽  
pp. 239-248
Author(s):  
Christopher R Anthony ◽  
Dana M Sanchez

2017 ◽  
Vol 48 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Zhao Zhai ◽  
Tuomas Ahola ◽  
Yun Le ◽  
Jianxun Xie

While the governance of Western megaprojects is indirectly influenced by governments through legislation and regulations, the Chinese state actively oversees and controls projects of societal importance. To provide clarity on the role of the state in Chinese megaprojects, we carried out a case study focusing on EXPO 2010 Shanghai. Our analysis revealed that through a project-specific organization Construction Headquarter (CHQ), the Chinese state executes administrative strength, forces authorities to temporarily integrate their processes for the benefit of the project, influences contractor and resource selection decisions, induces leadership accountability, and promotes shared project values.


2002 ◽  
Vol 6 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Bryan F. J. Manly

A resource selection probability function is a function that gives the prob- ability that a resource unit (e.g., a plot of land) that is described by a set of habitat variables X1 to Xp will be used by an animal or group of animals in a certain period of time. The estimation of a resource selection function is usually based on the comparison of a sample of resource units used by an animal with a sample of the resource units that were available for use, with both samples being assumed to be effectively randomly selected from the relevant populations. In this paper the possibility of using a modified sampling scheme is examined, with the used units obtained by line transect sampling. A logistic regression type of model is proposed, with estimation by conditional maximum likelihood. A simulation study indicates that the proposed method should be useful in practice.


2007 ◽  
Vol 34 (2) ◽  
pp. 77 ◽  
Author(s):  
Erik Klop ◽  
Janneke van Goethem ◽  
Hans H. de Iongh

The preference of grazing herbivores to feed on grass regrowth following savanna fires rather than on unburnt grass swards is widely recognised. However, there is little information on which factors govern patterns of resource selection within burnt areas. In this study, we attempted to disentangle the effects of different habitat and grass sward characteristics on the utilisation of post-fire regrowth by nine species of ungulates in a fire-dominated woodland savanna in north Cameroon. We used resource-selection functions based on logistic regression. Overall, the resource-selection functions identified the time elapsed since burning as the most influential parameter in determining probability of use by ungulates, as most species strongly selected swards that were recently burned. This pattern might be related to nutrient levels in the grass sward. In addition, most species selected areas with high grass cover and avoided grass swards with high amounts of dead stem material. This is likely to increase bite mass and, hence, intake rates. The avoidance of high tree cover by some species may suggest selection for open areas with good visibility and, hence, reduced risk of predation. Body mass seemed to have no effect on differential selection of post-fire regrowth, irrespective of feeding style.


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