Parametric Wavelength Conversion From Conventional Near-Infrared to Visible Band

The feasibility of distant wavelength conversion in photonic crystal fiber was explored. The one-pump parametric architecture was used to demonstrate translation from the conventional (1550 nm) to visible (500 nm) band. Experimental results are reported, which demonstrate the conventional-to-visible conversion of single- and multiple-channel signals encoded in the nonreturn-to-zero format     

A 60‐GHz on‐chip slot‐array antenna in integrated‐passive‐device technology for antenna‐in‐package applications

A new millimeter‐wave antenna structure on a low‐cost, production platform integrated passive device technology is presented. The antenna consists of a 2‐by‐1 array of slot antennas at 60 GHz. An in‐house developed on‐chip antenna measurement setup was used to characterize the fabricated antenna. The measurement results show an antenna gain of more than 5 dBi with a return loss of 18 dB at 60 GHz. The better‐than‐10‐dB impedance bandwidth of the antenna covers the 60‐GHz unlicensed band from 57 to 64 GHz. The 3‐dB beamwidths of the antenna are 105° and 76° at E‐plane and H‐plane at 60 GHz, respectively. The size of the die of the antenna is 2 mm × 4.5 mm. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:155–160, 2014.     

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.     

A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems

In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that our algorithm is efficient and robust.     

Predicting the spontaneous termination of atrial fibrillation based on poincare section in the electrocardiogram phase space

Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia. Predicting the conditions under which AF terminates spontaneously is an important task that would bring great benefit to both patients and clinicians. In this study, a new method was proposed to predict spontaneous AF termination by employing the points of section (POS) coordinates along a Poincare section in the electrocardiogram (ECG) phase space. The AF Termination Database provided by PhysioNet for the Computers in Cardiology Challenge 2004 was applied in the present study. It includes one training dataset and two testing datasets, A and B. The present investigation was initiated by producing a two-dimensional reconstructed phase space (RPS) of the ECG. Then, a Poincare line was drawn in a direction that included the maximum point distribution in the RPS and also passed through the origin of the RPS coordinate system. Afterward, the coordinates of the RPS trajectory intersections with this Poincare line were extracted to capture the local behavior related to the arrhythmia under investigation. The POS corresponding to atrial activity were selected with regard to the fact that similar ECG morphologies such as P waves, which are corresponding to atrial activity, distribute in a specific region of the RPS. Thirteen features were extracted from the selected intersection points to quantify their distributions. To select the best feature subset, a genetic algorithm (GA), in combination with a support vector machine (SVM), was applied to the training dataset. Based on the selected features and trained SVM, the performance of the proposed method was evaluated using the testing datasets. The results showed that 86.67% of dataset A and 80% of dataset B were correctly classified. This classification accuracy is in the same range as or higher than that of recent studies in this area. These results show that the proposed method, in which no complicated QRST cancelation algorithm was used, has the potential to predict AF termination.     

Thermogravimetric analysis and kinetic study of heavy oil pyrolysis

Pyrolysis, so-called devolatilization, is one of the first steps of all thermochemical processes occurring in an inert atmosphere. The authors discuss the main kinetic features of heavy oil pyrolysis, on the basis of the data derived m from a TGA analysis and by using a kinetic model. The samples were heated over a range of temperature from 400 K to 430°C at various heating rates between 10 and 80°C/min. Experimental results showed that the effect of time is considerable in the case of tar conversion, compared to char and gases.     

Interaction of α-Cyano-4-hydroxycinnamic Acid Drug with Inorganic BN Nanocluster: A Density Functional Study

We inspected the possibility of using a synthesized boron nitride nanocluster (B₁₂N₁₂) as a potential chemical sensor for anticancer α-cyano-4-hydroxycinnamic acid (CHC) drug by performing density functional theory calculations. It was found that CHC drug is mainly adsorbed via its acidic group on the BN nanocluster with adsorption energy about ??23.7 kcal/mol. In view of the high decrease of HOMO–LUMO energy gap (E_g) of the BN nanocluster after the adsorption process, it is expected that this process induces a significant increase in its electrical conductivity. Thus, the BN nanocluster is suggested as a potential sensor for CHC drug detection. We predicted that the BN cluster benefits from a short recovery time (~?22.7 s) and high sensitivity (55.2% decrease in the E_g). Also, it is interesting that the BN nanocluster can be used as a sensor in the pristine form without needing to the functionalization, doping, decoration, etc. Finally, we showed that by increasing the percentage of Hartree–Fock exchange of the functional, the adsorption energy, and sensitivity are increased and decreased, respectively. Also, the sensitivity of the BN nanocluster is predicted to decrease in water solution.     

Castor oil-based polyurethane coatings containing benzyl triethanol ammonium chloride: synthesis,characterization, and biological properties

In the present work, benzyl triethanol ammonium chloride (BTEAC) was employed as a reactive bactericidal additive for preparing of polyurethane coatings. In this regard, castor oil as a renewable resource-based polyol, polyethylene glycol (PEG1000), and BTEAC were reacted with toluene diisocyanate. Physical, mechanical, and thermal characteristics as well as biocompatibility and antibacterial properties of polyurethanes were evaluated. The prepared polyurethanes showed two-phase structure with soft and hard segments glass transition temperature transitions in the range of 18–70 and 85–153 °C, respectively. Initial modulus and tensile strength were improved for coatings with higher BTEAC content, while elongation at break and thermal stability were decreased. Hydrophilicity of coatings was increased for polyurethanes based on higher content of BTEAC and PEG1000. Polyurethanes with higher BTEAC content showed better cytocompatibility for mouse L929 fibroblast cells. Moreover, coatings with higher hydrophilicity and BTEAC content displayed superior antibacterial activity against both Escherichia coli and Staphylococcus aureus bacteria.     

Application of artificial neural network for vapor liquid equilibrium calculation of ternary system including ionic liquid: Water, ethanol and 1-butyl-3-methylimidazolium acetate

A feed forward three-layer artificial neural network (ANN) model was developed for VLE prediction of ternary systems including ionic liquid (IL) (water+ethanol+1-butyl-3- methyl-imidazolium acetate), in a relatively wide range of IL mass fractions up to 0.8, with the mole fractions of ethanol on IL-free basis fixed separately at 0.1, 0.2, 0.4, 0.6, 0.8, and 0.98. The output results of the ANN were the mole fraction of ethanol in vapor phase and the equilibrium temperature. The validity of the model was evaluated through a test data set, which were not employed in the training case of the network. The performance of the ANN model for estimating the mole fraction and temperature in the ternary system including IL was compared with the non-random-two-liquid (NRTL) and electrolyte non-random-two-liquid (eNRTL) models. The results of this comparison show that the ANN model has a superior performance in predicting the VLE of ternary systems including ionic liquid.