Many computational schemes in Linear Algebra can be studied from the point of view of TimeVarying Linear Systems theory. This approach not only puts a variety of results in a unified framework, but also generates new and unexpected results such as strong approximations of operators or matrices by computational networks of low complexity, and the embedding of contractive operations in orthogonal...

where P (t, λ) is a Poisson process with rate λ and Di for i ≥ 0 are i.i.d. random variables whose logarithm is uniformly distributed: logDi = U(log 0.25, log 32.0) between the logarithms of 0.25 and 32.0. This results in a distribution where the density is inversely proportional to the value. This is very important for the results below, because different distributions would emphasize either s...

The spread of an infection on a real-world social network is determined by the interplay of two processes – the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but d...

The EVs battery has the ability to enhance the balance between the load demand and power generation units. The EV aggregators to manage the random behaviour of EV owners and increasing EVs participation in the ancillary services market are employed. The presence of aggregators could lead to time-varying delay in load frequency control (LFC) schemes. The effects of these delays must be considere...

in this paper the numerical analysis of flow and time dependent heat transfer of micro-tube conveying nanofluid in laminar flow is investigated. in this study, convection heat transfer of nanofluid and base fluid and transient analysis for time-varying heat flux for time step of 0.0001 second are elucidated. it is observed that the pumping power of nanofluid flowing and the maximum temperature ...

Tensors are effective representations for complex and time-varying networks. The factorization of a tensor provides a high-quality low-rank compact basis for each dimension of the tensor, which facilitates the interpretation of important structures of the represented data. Many existing tensor factorization (TF) methods assume there is one tensor that needs to be decomposed to low-rank factors....