Principles of Optimal Design

Abstract

vor dem Hintergrund der neueren Entwicklungen im technischen (Explosionsschutz-) Recht.Einige theoretische Abhandlungen, z. B. über die physikalisch-chemischen Grundlagen, stellen durchaus hohe Anforderungen an den Leser, der vielleicht nicht Zeit und Muûe hat, sich mit den Hintergründen der Phänomene zu beschäftigen und dem diese entbehrlich erscheinen. Aber gerade die hier gelungene konzentrierte Zusammenstellung moderner wissenschaftlicher Erkenntnisse macht einen zusätzlichen Reiz des Buches aus.So ist das Buch ± insbesondere wegen des groûen zusammengetragenen Erfahrungs-und Wissensschatzes ± eine wertvolle Hilfe für den in der Praxis Tätigen, der mit Explosionsschutzproblemen der beschriebenen Art konfrontiert wird, genauso aber auch für denjenigen, der sich forschend mit dieser Thematik beschäftigt. Das Handbuch ist sicher seinen (nicht niedrigen) Preis mehr als wert!

Using multi-objective computational design to extend protein promiscuity

“…To compare two different solutions, a relation of dominance is defined as follows: A dominates B if, for all objectives, A has a better performance than B; as a result A has to be kept while B has to be discarded (even though B may lead to an active foldable protein). The set of all non-dominated solutions is called Pareto globally optimal set (or, simply, the Pareto Set) and finding this set is the goal of the multiobjective computational optimization problem [23]. Note that, in the Pareto Set, only those solutions showing an optimal trade-off between the different objectives are kept.…”

“…Both a measure of protein stability and de-novo catalytic activity are simultaneously optimized by using two competing score functions for folding free energy and binding free energy of the protein-ligand (i.e. transition-state-model) complex , obtaining the Pareto Set [23] of optimal stability/promiscuous-function solutions. The goal of this procedure is the development of the new activity with the lowest possible stability cost, which has two advantages.…”

Representational affordances in design, with examples from analogy making and optimization

“…Design optimization is a process that aims to find the "best" design with respect to a set of criteria (Papalambros and Wilde 2000). This process usually requires formulating a mathematical representation of the design problem in terms of an objective function, a set of equality and inequality constraints, design variables, and design parameters.…”

“…This process usually requires formulating a mathematical representation of the design problem in terms of an objective function, a set of equality and inequality constraints, design variables, and design parameters. We have adapted an example from Papalambros (1994) and Papalambros and Wilde (2000) that is concerned with optimizing the design of a linear actuator. Here, the actuator's drive screw (shown in black in Figure 9) needs to be dimensioned with the objective to minimize material cost.…”

“…For example, monotonicity analysis and dominance analysis are methods for discovering constraints that may be eliminated from the problem formulation, thus reducing the search space (Papalambros and Wilde 2000). Dimensional variable expansion (Cagan and Agogino 1991) can introduce new design variables by dividing the search space into multiple regions.…”