Case-control studies design description

Quality of evidence I, evidence from >1 properly randomized, controlled trial II, evidence from <1 well-designed clinical trial, without randomization from cohort or case-controlled analytic studies (preferably from >1 center) or from multiple time-series III, evidence from opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees. 

The description of the dynamic behaviour of the process is fundamental for the design of the control system. The required setpoints of the relevant process parameters must be known for devising the safety function (cf. Case Study 4.2). 

This section starts by presenting the outline of the methodology. It follows the description of a case study and the plantwide control problem. Possible control structures, as well as the effect of recycles are evaluated by linear MIMO controllability analysis, both at steady state and in the frequency domain. The comparison of design alternatives is performed by closed loop simulation. This procedure allows the designer to choose the final flowsheet, as well as the appropriate control strategy. 

Since process and properties are so closely linked in the production of emulsion polymers, a variety of processes have been devised as efforts to design and control the microstructure of latex particles have intensified. The central issue is whether, as a general rule, particle growth is better represented in terms of a surface growth model or of a bulk polymerization model. Results obtained by a variety of methods developed to study particle and film morphology will be reviewed, and the special case of water-soluble monomers will be considered along with descriptions of process techniques designed to control particle structure. 

This paper adopts a set-theory framework for the resilience analysis of interconnected infrastructure systems. A detailed description of the system dynamics modeling is provided and invariance properties are analyzed to define and identify the resilience region, as a function of the governing parameters. This allows controlling the characteristics of the system resilience properties by design, operation and maintenance properties as represented by the values of the related parameters. An illustration of the analytical power of the framework adopted is provided on an interconnected interconnected system case study, with detailed discussions of its behavior and response to failures and/or disturbances, based on simulation results. 

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