To control the workflow for surgical patients, we in-cooperate radio-frequency identification (RFID) technology to develop a Patient Advancement Monitoring System PAMSS) in operation theater.
The proposed logistics-oriented methodology integrates production routes design, buffers capacity assignment, and allocation of the dispatching rules that control the workflow as well as allows one to evaluate system's behavior including its steady state and transient state.
First, the control logic component receives the user's request and controls the workflow of the entire system.
These executive processes are necessary to control the workflow of activities taking place in working memory, for example selecting stimuli for momentary operations.
With no single computer controlling the overall workflow, it proves critical to store data in a centralized location.
This system controls the computational workflow, ending with the option to publish to pre-selected data repositories, one of which is the one being discussed here.
Hence, our framework controls the upper workflow management system and it is not necessary to perform additional configurations on such external systems (i.e., all configurations must go through F l u χ).
Also, the front-end makes it possible to control the simulation workflow from a remote machine.
QuickNGS takes advantage of powerful hardware architectures and a comprehensive database to control the overall workflow.
Thus, only the phenotype information has to be uploaded by the GRIMP user to the system, which controls the detailed workflow (Fig. 1).
As the sample metadata in the QuickNGS database are used to completely control the overall workflow, these have to be provided to the QuickNGS database before any analysis can be started.
