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Novel multi-objective optimisation methodologies, including a two-step sequential optimisation approach and multi-objective optimisation approaches using non-dominated sorting genetic algorithms (NSGAs) and MATLAB based linear programming integrated with genetic algorithms have been developed for the first time to engineer the cellular metabolic productivity and process performance simultaneously.
However, some recent studies have failed to achieve change in either selective attention or emotional vulnerability using ABM methodologies, including a recent randomised controlled trial by Carlbring et al. Some have sought to represent such absence of evidence as a sound basis not to further pursue ABM as an online intervention.
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Our methodologies include a condition to check whether the timing constraints can be met, a condition for the existence of an optimal solution to the HPC problem and a multi-layer contract net protocol to find the minimal cost solution.
Potential benefits of sequence driven methodologies include a more rapid turnaround time (Quail et al., 2012), combined with a largely unbiased approach in species detection, including the opportunity for unexpected discoveries.
Other study methodologies included a case-control study in Niagara County, New York; a cross-sectional survey in a rural New Jersey community; and an analysis of migration patterns among children in Great Britain [ 10, 18, 20].
Methodologies included: a) Stories reported by people with CFS/ME themselves (published 'own stories'); or b) Primary research where researchers set out to assess needs of people with CFS/ME; or c) Primary research not specifically designed to examine needs (e.g., designed to assess impact or experiences of living with CFS/ME), but where expressed needs of people with CFS/ME were described.
Section 4 describes the novel clustering methodology, including a small illustrative example.
To this end, a complex statistically based methodology including a decision support system was developed and implemented.
Therefore, a design methodology including a rigorous treatment of the influence of uncertainty is essential for the design of plant-wide waste management strategies at multi-purpose plants.
Three numerical examples are employed to demonstrate the effectiveness of the proposed methodology, including a mathematic problem with explicit expression and two engineering applications in acoustic field.
The experimental design parameters were derived in the half-scale model for the assessment of the design analysis methodology including a CFD tool.
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