Summarizing is the practice of condensing a body material into more manageable size while retaining all key data elements and intended meaning. Automatic text summarizing systems can now quickly retrieve summary phrases from input documents. However, it has number shortcomings, such as duplication, insufficient coverage, incorrect extraction lines, poor sentence coherence. In this study, new co...

Modeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained ...

We propose to use chance constrained programming for process optimization and control under uncertainty. The stochastic property of the uncertainties is included in the problem formulation. The output constraints are to be ensured with a predefined confidence level. The problem is then transformed to an equivalent deterministic NLP problem. The solution of the problem has the feature of predict...

We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains “simple” weight vectors which most of are accurate on the training set. Two versions of the learning process are cast as convex optimization problems, and it is shown how to sol...

The design of an efficient energy management system (EMS) for monopolar DC networks with high penetration photovoltaic generation plants is addressed in this research through a convex optimization point view. EMS formulated as multi-objective problem that involves economic, technical, and environmental objective functions subject to typical constraints regarding power balance equilibrium, therm...