[en] Published in summary form only

[en] The structure of human calcitonin gene-related peptide 1 (hCGRP-1) has been determined by ¹H NMR in a mixed-solvent system of 50% trifluoroethanol/50% H₂O at pH 3.7 and 27 degree C. Complete resonance assignment was achieved by using two-dimensional methods. Distance restraints for structure calculations were obtained by semiquantitative analysis of intra- and interresidue nuclear Overhauser effects; in addition, stereospecific or χ¹ rotamer assignments were obtained for certain side chains. Structures were generated from the distance restraints by distance geometry, followed by refinement using molecular dynamics, and were compared with experimental NH-CαH coupling constants and amide hydrogen exchange data. The structure of hCGRP-1 in this solvent comprises an amino-terminal disulfide-bonded loop (residues 2-7) leading into a well-defined α-helix between residues 8 and 18; thereafter, the structure is predominantly disordered, although there are indications of a preference for a turn-type conformation between residues 19 and 21. Comparison of spectra for the homologous hCGRP-2 with those of hCGRP-1 indicates that the conformations of these two forms are essentially identical

[en] In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into 'families'. On the basis of the decision maker's preferences, each family is then synthetically represented by a 'head of the family' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions

[en] Published in summary form only

[en] In this paper, a framework is developed for identifying a limited number of representative solutions of a multiobjective optimization problem concerning the inspection intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are first clustered into 'families', which are then synthetically represented by a 'head of the family' solution. Three clustering methods are analyzed. Level Diagrams are then used to represent, analyse and interpret the Pareto Fronts reduced to their head-of-the-family solutions. Two decision situations are considered: without or with decision maker preferences, the latter implying the introduction of a scoring system to rank the solutions with respect to the different objectives: a fuzzy preference assignment is then employed to this purpose. The results of the application of the framework of analysis to the problem of optimizing the inspection intervals of a nuclear power plant safety system show that the clustering-based reduction maintains the Pareto Front shape and relevant characteristics, while making it easier for the decision maker to select the final solution.

[en] Reliability-based and risk-informed design, operation, maintenance and regulation lead to multiobjective (multicriteria) optimization problems. In this context, the Pareto Front and Set found in a multiobjective optimality search provide a family of solutions among which the decision maker has to look for the best choice according to his or her preferences. Efficient visualization techniques for Pareto Front and Set analyses are needed for helping decision makers in the selection task. In this paper, we consider the multiobjective optimization of system redundancy allocation and use the recently introduced Level Diagrams technique for graphically representing the resulting Pareto Front and Set. Each objective and decision variable is represented on separate diagrams where the points of the Pareto Front and Set are positioned according to their proximity to ideally optimal points, as measured by a metric of normalized objective values. All diagrams are synchronized across all objectives and decision variables. On the basis of the analysis of the Level Diagrams, we introduce a procedure for reducing the number of solutions in the Pareto Front; from the reduced set of solutions, the decision maker can more easily identify his or her preferred solution.