Khalifa University

About: Khalifa University is a education organization based out in Abu Dhabi, United Arab Emirates. It is known for research contribution in the topics: Computer science & Adsorption. The organization has 3752 authors who have published 10909 publications receiving 141629 citations.

Asymmetric Magnet-Based Nonlinear Energy Sink

Abstract: The nonlinear energy sink (NES) is a light-weighted device used for shock mitigation in dynamic structures through its passive targeted energy transfer (TET) mechanism. Here, a new design for the NES is introduced based on using an asymmetric NES force. This force is strongly nonlinear in one side of the NES equilibrium position, whereas it is either weakly nonlinear or weakly linear in the other side. This is achieved by introducing the asymmetric magnet-based NES in which the asymmetric nonlinear magnetic repulsive force is generated by two pairs of aligned permanent magnets. Consequently, this proposed design is found to provide a considerable enhancement in the shock mitigation performance compared with the symmetric stiffness-based NESs for broadband energy inputs.

Optimal experimental design for gene regulatory networks in the presence of uncertainty

Abstract: Of major interest to translational genomics is the intervention in gene regulatory networks (GRNs) to affect cell behavior; in particular, to alter pathological phenotypes. Owing to the complexity of GRNs, accurate network inference is practically challenging and GRN models often contain considerable amounts of uncertainty. Considering the cost and time required for conducting biological experiments, it is desirable to have a systematic method for prioritizing potential experiments so that an experiment can be chosen to optimally reduce network uncertainty. Moreover, from a translational perspective it is crucial that GRN uncertainty be quantified and reduced in a manner that pertains to the operational cost that it induces, such as the cost of network intervention. In this work, we utilize the concept of mean objective cost of uncertainty (MOCU) to propose a novel framework for optimal experimental design. In the proposed framework, potential experiments are prioritized based on the MOCU expected to remain after conducting the experiment. Based on this prioritization, one can select an optimal experiment with the largest potential to reduce the pertinent uncertainty present in the current network model. We demonstrate the effectiveness of the proposed method via extensive simulations based on synthetic and real gene regulatory networks.