Mini-maximizing two qubit quantum computations

Two qubit quantum computations are viewed as two player, strictly competitive games and a game-theoretic measure of optimality of these computations is developed. To this end, the geometry of Hilbert space of quantum computations is used to establish the equivalence of game-theoretic solution concepts of Nash equilibrium and mini-max outcomes in games of this type, and quantum mechanisms are designed for realizing these mini-max outcomes.     

Comorbid anxiety disorders and treatment of depression in people with multiple sclerosis.

Objective: Anxiety is highly comorbid with depression, but little is known about the impact of anxiety disorders on the effectiveness of empirically supported psychotherapies for depression. We examined such outcomes for people with Multiple Sclerosis (MS) and depression, with versus without comorbid anxiety disorders. Design: Participants with MS (N = 102) received 16 weeks of telephone-administered psychotherapy for depression and were followed for one year post-treatment. Results: Participants with comorbid anxiety disorders improved to a similar degree during treatment as those without anxiety disorders. Outcomes during follow-up were mixed, and thus we divided the anxiety diagnoses into distress and fear disorders. The distress disorder (GAD) was associated with elevated anxiety symptoms during and after treatment. In contrast, fear disorders (i.e., panic disorder, agoraphobia, social phobia, specific phobia) were linked to depression, specifically during follow-up, across 3 different measures. Conclusions: People with GAD receiving treatment for depression may benefit from additional services targeting anxiety more specifically, while those with comorbid fear disorders may benefit from services targeting maintenance of gains after treatment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computationally efficient 2D beamspace matrix pencil method for direction of arrival estimation

In this paper, we propose a new 2-dimensional beamspace matrix pencil (2D BMP) method for direction of arrival (DOA) estimation of plane wave signals using a uniform rectangular array (URA). Based on some a priori information about DOA, the proposed method transforms the complex signal subspace in 2D matrix pencil (2D MP) method [Y. Hua, Estimating two-dimensional frequencies by matrix enhancement and matrix pencil, IEEE Trans. Signal Process. 40 (9) (1992) 2267–2280] into a real and reduced dimensional beamspace using the discrete Fourier transform (DFT) matrix transformation. Consequently, the computational complexity is reduced (several times) in comparison with 2D MP method. Computer simulations are provided to show that 2D BMP method gives comparable performance in terms of average mean square error of the estimated DOA with lesser floating point operations as compared to the existing (MP) methods.     

TSDC spectra in negatively corona charged ABS electrets

Summary Corona charging technique has been used during polymer electret formation. Investigations of the thermally stimulated depolarization current (TSDC) in the electrets formed from Acrylonitrile-Butadiene-Styrene (ABS) terpolymer films were carried out. The optimum conditions for negatively corona-charged ABS films were determined. With the aid of partial heating technique, a single peak was obtained at T=381±2 K. This peak is associated with the temperature which was attributed to the motion of the bulk detrapping electrons arising from the thermal agitation as well as from erosion of the traps by the molecular motion. The activation energy of the process has been calculated. The electret property can be described in terms of dipolar relaxation process.     

Modulation of nanotube formation in apatite single crystal via organic molecule incorporation

Hydroxyapatite materials are potentially useful for biomedical application, especially as vehicles for functional molecules. Structural control of bulk apatite materials, such as in the fabrication of hollow microspheres or porous structures, has been studied for this purpose. However, control of the internal structure of the source apatite crystal itself is still a challenge. Here, we show that small organic molecules incorporated in apatite crystals act as porogens which control the porous structure of apatite single crystal. The presence of amino acid under apatite synthesis conditions leads to firm bindings and encapsulation of the amino acid in apatite single crystals. Amino acid elimination by heating or electron beam irradiation enhances the pore formation in apatite single crystal. Moreover, incorporation of an acidic amino acid in apatite induces peapod like nanotubes in apatite single crystals. This study suggests the potential of using small organics for nano-structural control of apatite single crystals which would be valuable for enhancing drug loadings or modulating material digestion in vivo.     

Chemical composition and antioxidant properties of candlenut oil extracted by supercritical CO2

Candlenut oil was extracted using supercritical CO(2) (SC-CO(2)) with an optimization of parameters, by the response surface methodology. The ground candlenut samples were treated in 2 different ways, that is, dried in either a heat oven (sample moisture content of 2.91%) or dried in a vacuum oven (sample moisture content of 1.98%), before extraction. An untreated sample (moisture content of 4.87%) was used as a control. The maximum percentage of oil was extracted from the heat-oven-dried sample (77.27%), followed by the vacuum-oven-dried sample (74.32%), and the untreated sample (70.12%). At an SC-CO(2) pressure of 48.26 Mpa and 60 min of extraction time, the optimal temperatures for extraction were found to be 76.4 °C, 73.9 °C, and 70.6 °C for the untreated, heat-oven-dried, and vacuum-oven-dried samples, respectively. The heat-oven-dried sample contains the highest percentage of linoleic acid, followed by the untreated and vacuum-oven-dried samples. The antiradical activity of candlenut oil corresponded to an IC(50) value of 30.37 mg/mL.     

Bioaugmented membrane bioreactor (MBR) with a GAC-packed zone for high rate textile wastewater treatment

The long-term performance of a bioaugmented membrane bioreactor (MBR) containing a GAC-packed anaerobic zone for treatment of textile wastewater containing structurally different azo dyes was observed. A unique feeding strategy, consistent with the mode of evolution of separate waste streams in textile plants, was adopted to make the best use of the GAC-zone for dye removal. Dye was introduced through the GAC-zone while the rest of the colorless media was simultaneously fed through the aerobic zone. Preliminary experiments confirmed the importance of coupling the GAC-amended anaerobic zone to the aerobic MBR and also evidenced the efficacy of the adopted feeding strategy. Following this, the robustness of the process under gradually increasing dye-loading was tested. The respective average dye concentrations (mg/L) in the sample from GAC-zone and the membrane-permeate under dye-loadings of 0.1 and 1 g/L.d were as follows: GAC-zone (3, 105), permeate (0, 5). TOC concentration in membrane-permeate for the aforementioned loadings were 3 and 54 mg/L, respectively. Stable decoloration along with significant TOC removal during a period of over 7 months under extremely high dye-loadings demonstrated the superiority of the proposed hybrid process.     

A robust neural network model for fault detection in the presence of mislabelled data

Several data-driven methodologies for process monitoring and detection of faults or abnormalities have been developed for the safety of processing systems. The effectiveness of data-based models, however, is impacted by the volume and quality of training data. This work presents a robust neural network model for addressing the mislabelled and low-quality data in detecting faults and process abnormalities. The approach is based on harnessing data quality features along with supervisory labels in the network training. The data quality has been computed using the Mahalanobis distances and trusted centres of each class of data such as normal and faulty data. The method has been examined for detecting abnormalities in two case studies; a continuous stirred tank heater problem for detecting leaks and the Tennessee Eastman chemical process for detecting step and sticking faults. The performance of the proposed robust artificial neural networks (ANN) model is evaluated in terms of accuracy, fault detection rate, false alarm rate, and classification index at varying extents of mislabelling, namely, 1%, 5%, and 10% mislabelled data. The proposed model demonstrates higher detection performance, especially at increased labels of mislabelled data where the performance of the conventional ANN is severely impacted. The proposed methodology can be advantageous in handling mislabelled and low-quality data issues which are crucial in the data-driven modelling of processing systems.