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Network area size represents the x and y dimensions of the network area.
Table 3 shows characteristics of network learning; in particular, network learning contexts, approaches and outcomes are considered as dimensions of the network learning.
Striking a balance between accommodating the growing complexity, sophistication and the constrained properties of the physical dimensions of the network is a constant challenge.
In this paper, we extend to the design of electric power distribution networks the constructal method of deducing the multiple dimensions of the network from the maximization of global system performance.
Applying principal component analysis (PCA) decreased the dimensions of the network's inputs and led to simpler architecture.
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This is statistically advantageous as the effective dimensions of the networks over the clusters are greatly lower than those over the genes.
Feature selection using fuzzy curves has been employed to reduce the dimension of the network.
Our analysis indicates that the fractal dimension of the network of slit-like conduits is smaller than that of the circular conduits for each sample.
The structural dimension of the network is the only area where there is some degree of convergence between output quantity and quality; here, we find a prevalence for the role of brokerage over cohesion.
Other elements that increase the complexity of the problem are the large dimension of the network and the interconnection with the timetables of boundary countries, due to international trains.
The loss statistics depend on the IP-RAN topology chosen, the traffic and user mobility patterns, the dimensioning of the network, as well as the QoS and IP mobility architecture chosen (overprovisioning, pure DiffServ, or QoS routing).
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