Since the core of system dynamics is to self-adjust node activity states and connections, we focus the mathematical description on these two behaviors of a node.
In order to test these connections, we first focused on an analysis of the well-described fungal lipase/feruloyl esterase A family.
Because the CAs-CHIP enabled easy connection of different functional capillaries, we focused on this device as an on-line sample-pretreatment attachment for CE separation capillaries in a manner similar to the previously reported FI-CE system.
To investigate the network connections for a known gene we focused on tumor necrosis factor (TNF), the most highly connected gene in the NSGG network with a known role in BMD.
Therefore, we assume that these online algorithms have perfect knowledge of the link between the vehicle and available APs, e.g., the effective bit rate and connection time informationb, so that we focus on characterizing their difference induced from their different handoff criteria.
First, we focus on the connection with the asymptotic stability properties of (1).
In this review, however, we focus on the connection between endomembranes (see Box 2) and the PTGS activities of miRNAs and siRNAs.
By this, we focus on the connections by which the actors attracted the new actors; hence, we can easily measure the actors' attractiveness.
We focus on how connections are established, regardless of the reason behind it, whether it is mobility, availability, scheduling, or interference.
In contrast, rather than compute the entire response network, we focus on discovering connections between perturbed biological processes.
