Hydrogen-driven denitrification of wastewater in an anaerobic submerged membrane bioreactor: potential for water reuse.

An anaerobic submerged membrane bioreactor was coupled with a novel hydrogen delivery system for hydrogenotrophic denitrification of municipal final effluent containing nitrate. The biological treatment unit and hydrogen delivery unit were proven successful in removing nitrate and delivering hydrogen, respectively. Complete hydrogen transfer resulted in reducing nitrate below detectable levels at a loading of 0.14 kg Nm(-3) d(-1). The produced water met all drinking water guidelines except for color and organic carbon. However, the organic carbon was removed by 72% mostly by membrane rejection. To reduce the organic carbon and color of the effluent, post treatment of the produced water is required.     

Anthocyanins characterization of 15 Iranian pomegranate (Punica granatum L.) varieties and their variation after cold storage and pasteurization

Anthocyanins (ACs) are phenolic compounds that are distributed widely in fruits and vegetables. Apart from imparting color to plants, ACs also have an array of health-promoting benefits. In this research, the amounts of major ACs of 15 pomegranate (Punica granatum L.) varieties obtained from Yazd province were determined. The major ACs detected in the studied varieties were as follows: delphinidin 3-glucoside (2.19–16.29 mg/L), delphinidin 3,5-diglucoside (2.36–63.07 mg/L), pelargonidin 3-glucoside (0.26–1.36 mg/L), pelargonidin 3,5-diglucoside (0.01–8.11 mg/L), cyanidin 3-glucoside (5.78–30.38 mg/L), and cyanidin 3,5-diglucoside (4.39–166.32 mg/L). The effect of storage time of unprocessed and pasteurized juices on ACs content of four selected varieties was also studied. Average degradation percentage of each AC was between 23.0 and 83.0% during 10 days at 4 °C. Moreover, in pasteurized juices average degradation of ACs was 42.8 ± 0.5% after 10 weeks storage at 4 °C.     

Pd/Cu-Free Cobalt-Catalyzed Suzuki and Heck Using Green Bio-Magnetic Hybrid and DFT-Based Theoretical Study

Catalysis Letters - Several highly efficient and magnetically recyclable cobalt catalytic systems were prepared using magnetic chitosan and some safe and available organic compounds...     

Quantifying nonclassicality of correlations based on the concept of nondisruptive local state identification

A bipartite state is classical with respect to party A if and only if party A can perform nondisruptive local state identification (NDLID) by a projective measurement. Motivated by this we introduce a class of quantum correlation measures for an arbitrary bipartite state. The measures utilize the general Schatten p-norm to quantify the amount of departure from the necessary and sufficient condition of classicality of correlations provided by the concept of NDLID. We show that for the case of Hilbert–Schmidt norm, i.e., \(p=2\), a closed formula is available for an arbitrary bipartite state. The reliability of the proposed measures is checked from the information-theoretic perspective. Also, the monotonicity behavior of these measures under LOCC is exemplified. The results reveal that for the general pure bipartite states these measures have an upper bound which is an entanglement monotone in its own right. This enables us to introduce a new measure of entanglement, for a general bipartite state, by convex roof construction. Some examples and comparison with other quantum correlation measures are also provided.     

Face recognition using adaptively weighted patch PZM array from a single exemplar image per person

Though numerous approaches have been proposed for face recognition, little attention is given to the moment-based face recognition techniques. In this paper we propose a novel face recognition approach based on adaptively weighted patch pseudo Zernike moment array (AWPPZMA) when only one exemplar image per person is available. In this approach, a face image is represented as an array of patch pseudo Zernike moments (PPZM) extracted from a partitioned face image containing moment information of local areas instead of global information of a face. An adaptively weighting scheme is used to assign proper weights to each PPZM to adjust the contribution of each local area of a face in terms of the quantity of identity information that a patch contains and the likelihood of a patch is occluded. An extensive experimental investigation is conducted using AR and Yale face databases covering face recognition under controlled/ideal conditions, different illumination conditions, different facial expressions and partial occlusion. The system performance is compared with the performance of four benchmark approaches. The encouraging experimental results demonstrate that moments can be used for face recognition and patch-based moment array provides a novel way for face representation and recognition in single model databases.     

Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

A dual‐broadband circularly polarized halved falcate‐shape printed monopole antenna

A halved falcate‐shape dual‐broadband circularly polarized printed monopole antenna is proposed. To generate the equal amplitude orthogonal modes, two halved falcate‐shaped antenna are used. Also, to provide the 90° phase difference between the two modes, three stubs are used in the ground plane of the antenna. The proposed antenna provides 22.6 (1.36–1.72 GHz) and 44.4% (5.25–8.25 GHz) 3 dB axial ratio bandwidth over the lower and upper bands, respectively. By adjusting the parameters of the antenna, the lower and upper band center frequencies can be tuned individually. The proposed antenna is fabricated, and results are compared with those of the simulation. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm

A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO-SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability of SQP is weak to escape local optimum but can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. Hence, in the proposed method, at the beginning stage of search process, a PSO algorithm is employed to find a near optimum solution. In this case, an improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm. When the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To validate the performance of the proposed IPSO-SQP approach, it is evaluated on two optimal control problems. Results show that the performance of the proposed algorithm is satisfactory.     

The utilization of soft transformation and genetic algorithm to model two sources of uncertainty of Indicator Kriging

Indicator Kriging (IK) is a geostatistical method that uses observation points to quantify the probabilities at which a set of thresholds are exceeded at unmeasured points. To improve IK accuracy, the interpolation process should consider its uncertainty sources. By doing this, we also maintain its ability to provide the conditional cumulative distribution function (ccdf), which is a reliable measure of local uncertainty. This study modeled two IK uncertainty sources, i.e., measurement errors attached to observation points and subjective threshold choices. Soft Indicator Kriging (SIK), which uses a soft transformation for observation points, considers the measurement errors of these two sources. To select the thresholds objectively, a genetic algorithm (GA) was performed to obtain the optimum set of thresholds related to an objective function, which minimized the mean absolute error (MAE).     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.