Effects of Fermion Flavor on Exciton Condensation in Double Layer Systems

We use fermionic path integral quantum Monte Carlo to study the effects of fermion flavor on the physical properties of dipolar exciton condensates in double layer systems. We find that by including spin in the system weakens the effective interlayer interaction strength, yet this has very little effect on the Kosterlitz-Thouless transition temperature. We further find that, to obtain the correct description of screening, it is necessary to account for correlation in both the interlayer and intralayer interactions. We show that while the excitonic binding cannot completely surpress screening by additional fermion flavors, their screening effectiveness is reduced leading to a much higher transition temperatures than predicted with large-N analysis.Comment: 4 pages, 3 figure

A 100 kW experimental wind turbine: Simulation of starting, overspeed, and shutdown characteristics

The ERDA/NASA 100 kW experimental wind turbine is modeled on a digital computer in order to study the performance of a wind turbine under operating conditions. Simulation studies of starting, overspeed, and shutdown performance were made. From these studies operating procedures, precautions, and limitations are prescribed

Prediction of protein-protein interactions using one-class classification methods and integrating diverse data

This research addresses the problem of prediction of protein-protein interactions (PPI) when integrating diverse kinds of biological information. This task has been commonly viewed as a binary classification problem (whether any two proteins do or do not interact) and several different machine learning techniques have been employed to solve this task. However the nature of the data creates two major problems which can affect results. These are firstly imbalanced class problems due to the number of positive examples (pairs of proteins which really interact) being much smaller than the number of negative ones. Secondly the selection of negative examples can be based on some unreliable assumptions which could introduce some bias in the classification results. Here we propose the use of one-class classification (OCC) methods to deal with the task of prediction of PPI. OCC methods utilise examples of just one class to generate a predictive model which consequently is independent of the kind of negative examples selected; additionally these approaches are known to cope with imbalanced class problems. We have designed and carried out a performance evaluation study of several OCC methods for this task, and have found that the Parzen density estimation approach outperforms the rest. We also undertook a comparative performance evaluation between the Parzen OCC method and several conventional learning techniques, considering different scenarios, for example varying the number of negative examples used for training purposes. We found that the Parzen OCC method in general performs competitively with traditional approaches and in many situations outperforms them. Finally we evaluated the ability of the Parzen OCC approach to predict new potential PPI targets, and validated these results by searching for biological evidence in the literature

Domain discovery method for topological profile searches in protein structures

We describe a method for automated domain discovery for topological profile searches in protein structures. The method is used in a system TOPStructure for fast prediction of CATH classification for protein structures (given as PDB files). It is important for profile searches in multi-domain proteins, for which the profile method by itself tends to perform poorly. We also present an O(C(n)k +nk2) time algorithm for this problem, compared to the O(C(n)k +(nk)2) time used by a trivial algorithm (where n is the length of the structure, k is the number of profiles and C(n) is the time needed to check for a presence of a given motif in a structure of length n). This method has been developed and is currently used for TOPS representations of protein structures and prediction of CATH classification, but may be applied to other graph-based representations of protein or RNA structures and/or other prediction problems. A protein structure prediction system incorporating the domain discovery method is available at http://bioinf.mii.lu.lv/tops/

Design of wideband vibration-based electromagnetic generator by means of dual-resonator

This paper describes the design of a wideband electromagnetic energy harvester that utilizes a novel dual-resonator method to improve the operational frequency range of the vibration-based generator. The device consists of two separate resonator systems (coil and magnet), which each comply with their respective resonance frequencies. This is because both resonators are designed in such a way that both magnet and coil components will oscillate at an additive phase angle, and hence create greater relative motion between the two dominating resonance frequencies, which realizes the wideband generator. Each resonator system consists of a distinctive cantilever beam, one attached with four magnets and steel keepers, the other attached with a copper coil and stainless steel holder as the free end mass. Both cantilevers are clamped and fitted to a common base that is subjected to a vibration source. Basic analytical models are derived and a numerical model is implemented in MATLAB-Simulink. Electromagnetic, structural modal and static mechanical analysis for the design of the prototype are completed using ANSYS finite element tools. For a 0.8 m s−2 acceleration, the open-loop voltage obtained from the experiment shows a good correlation with those from the simulation. Peak induced voltage is measured to be 259.5Vrms as compared to 240.9Vrms from the simulator at 21.3 Hz, which implies an error range of 7.7%. The results also indicate that there is a maximum of 58.22% improvement in the induced voltage within the intermediate region which occurs at the intersection point between the output response plots of two single resonator generators