The European Coal and Steel Community (ECSC) was the first step in the process of European integration. Its founders had lofty aspirations that integration in the coal and steel would spill into a larger endeavor, and early scholarly analyses suggested that coal and steel integration was spurring more fundamental political...
In this essay, I will focus on the fifth theme of the Collaborative Learning Initiative: Reclaiming Security. Attempts to reclaim security in many African countries, tragically, often lead to greater insecurity as rulers respond by heightening repression. Some even close down access to social media and global communications thereby harming...
Recent advances in cell-free gene expression (CFE) systems have enabled their use for a host of synthetic biology applications, particularly for rapid prototyping of genetic circuits and biosensors. Despite the proliferation of cell-free protein synthesis platforms, the large number of currently existing protocols for making CFE extracts muddles the collective...
Patrick F. Quinn states that Edgar Allan Poe wrote poems at an age “too young to have any knowledge of the world but from his own breast,” and attributes Poe’s decision to leave flaws in his “smaller pieces” intact to “[fondness fostered by] his old age” (Quinn 9). While readers...
Robust optimization is a distinct approach to optimizations problems that allows for the incorporation of
uncertainty. The usefulness of robust optimization lies in the ability to solve for every realization of the uncertain
parameters. As a result, the problem can be solved for the worst-case scenarios of the entire set...
Traditionally, robust optimization has solved problems based on static decisions which are predetermined by the
decision makers. Once the decisions were made, the problem was solved and whenever a new uncertainty was
realized, the uncertainty was incorporated to the original problem and the entire problem was solved again to
account...
Robust optimization is a subset of optimization theory that deals with a certain measure of robustness vs uncertainty. This balance of robustness
and uncertainty is represented as variability in the parameters of the problem at hand and or its solution [1]. In robust optimization, the modeler
aims to find decisions...
Fuzzy programming is one of many optimization models that deal with optimization under uncertainty. This model can be applied when situations are not clearly
defined and thus have uncertainty, or an exact value is not critical to the problem. For example, categorizing people into young, middle aged and old is...
The chance-constrained method is one of the major approaches to solving optimization problems under various
uncertainties. It is a formulation of an optimization problem that ensures that the probability of meeting a certain
constraint is above a certain level. In other words, it restricts the feasible region so that the...