Polymer cosolvent system

Summary Both preferential adsorption coefficient λ and intrinsic viscosity [η] for poly (di-o-alkylphenyl methacrylates) /Tetrahydrofuran (THF)/water have been determined. The results are discussed in relation to the steric effect of the ortho-substituent groups on the aromatic ring.     

Multiple responses analysis and modeling of Fenton process for treatment of high strength landfill leachate

Landfill leachate is one of the most recalcitrant wastes for biotreatment and can be considered a potential source of contamination to surface and groundwater ecosystems. In the present study, Fenton oxidation was employed for degradation of stabilized landfill leachate. Response surface methodology was applied to analyze, model and optimize the process parameters, i.e. pH and reaction time as well as the initial concentrations of hydrogen peroxide and ferrous ion. Analysis of variance showed that good coefficients of determination were obtained (R2 > 0.99), thus ensuring satisfactory agreement of the second-order regression model with the experimental data. The results indicated that, pH and its quadratic effects were the main factors influencing Fenton oxidation. Furthermore, antagonistic effects between pH and other variables were observed. The optimum H2O2 concentration, Fe(II) concentration, pH and reaction time were 0.033 mol/L, 0.011 mol/L, 3 and 145 min, respectively, with 58.3% COD, 79.0% color and 82.1% iron removals.     

Weighted and extended total variation for image restoration and decomposition

In various information processing tasks obtaining regularized versions of a noisy or corrupted image data is often a prerequisite for successful use of classical image analysis algorithms. Image restoration and decomposition methods need to be robust if they are to be useful in practice. In particular, this property has to be verified in engineering and scientific applications. By robustness, we mean that the performance of an algorithm should not be affected significantly by small deviations from the assumed model. In image processing, total variation (TV) is a powerful tool to increase robustness. In this paper, we define several concepts that are useful in robust restoration and robust decomposition. We propose two extended total variation models, weighted total variation (WTV) and extended total variation (ETV). We state generic approaches. The idea is to replace the TV penalty term with more general terms. The motivation is to increase the robustness of ROF (Rudin, Osher, Fatemi) model and to prevent the staircasing effect due to this method. Moreover, rewriting the non-convex sublinear regularizing terms as WTV, we provide a new approach to perform minimization via the well-known Chambolle's algorithm. The implementation is then more straightforward than the half-quadratic algorithm. The behavior of image decomposition methods is also a challenging problem, which is closely related to anisotropic diffusion. ETV leads to an anisotropic decomposition close to edges improving the robustness. It allows to respect desired geometric properties during the restoration, and to control more precisely the regularization process. We also discuss why compression algorithms can be an objective method to evaluate the image decomposition quality.     

Online learning of task-driven object-based visual attention control

We propose a biologically-motivated computational model for learning task-driven and object-based visual attention control in interactive environments. In this model, top-down attention is learned interactively and is used to search for a desired object in the scene through biasing the bottom-up attention in order to form a need-based and object-driven state representation of the environment. Our model consists of three layers. First, in the early visual processing layer, most salient location of a scene is derived using the biased saliency-based bottom-up model of visual attention. Then a cognitive component in the higher visual processing layer performs an application specific operation like object recognition at the focus of attention. From this information, a state is derived in the decision making and learning layer. Top-down attention is learned by the U-TREE algorithm which successively grows an object-based binary tree. Internal nodes in this tree check the existence of a specific object in the scene by biasing the early vision and the object recognition parts. Its leaves point to states in the action value table. Motor actions are associated with the leaves. After performing a motor action, the agent receives a reinforcement signal from the critic. This signal is alternately used for modifying the tree or updating the action selection policy. The proposed model is evaluated on visual navigation tasks, where obtained results lend support to the applicability and usefulness of the developed method for robotics.     

Quantum dot light emitting devices for photomedical applications

While OLEDs have struggled to find a niche lighting application that can fully take advantage of their unique form factors as thin, flexible, lightweight and uniformly large‐area luminaire, photomedical researchers have been in search of low‐cost, effective illumination devices with such form factors that could facilitate widespread clinical applications of photodynamic therapy (PDT) or photobiomodulation (PBM). Although existing OLEDs with either fluorescent or phosphorescent emitters cannot achieve the required high power density at the right wavelength windows for photomedicine, the recently developed ultrabright and efficient deep red quantum dot light emitting devices (QLEDs) can nicely fit into this niche. Here, we report for the first time the in‐vitro study to demonstrate that this QLED‐based photomedical approach could increase cell metabolism over control systems for PBM and kill cancerous cells efficiently for PDT. The perspective of developing wavelength‐specific, flexible QLEDs for two critical photomedical fields (wound repair and cancer treatment) will be presented with their potential impacts summarized. The work promises to generate flexible QLED‐based light sources that could enable the widespread use and clinical acceptance of photomedical strategies including PDT and PBM.     

A novel and developed approximation for motion of a spherical solid particle in plane coquette fluid flow

The present article solves the couple equations of a spherical solid particle’s motion in plane coquette fluid flow by using the HPM-Padé technique which is a combination of the Homotopy Perturbation Method (HPM) and Padé approximation. The series solutions of the couple equations are developed. Generally, the truncated series solution is adequately in a small region and to overcome this limitation, the Padé techniques which have the advantage of turning the polynomial approximation into a rational function, are applied to the series solution to improve the accuracy and enlarge the convergence domain. The current results compared with those derived from HPM and the established fourth order Runge–Kutta method in order to ascertain the accuracy of the proposed method. It is found that this method can achieve more suitable results in comparison to HPM.     

Recent Advances in CNT-based FET Transistor Biosensors to Detect Biomarkers of Clinical Significance

Silicon - The development of advanced materials has attracted interest for promising novel applications in biosensing and realm of nanophotonics. This interest in nanoelectronics, and...     

Generation of motional entangled coherent state in an optomechanical system in the single photon strong coupling regime

The single-photon strong coupling in the deep-resolved sideband of the optomechanical system induces photon blockade (PB) effect. For the PB cavity, an initial mechanical coherent state evolves into superposition of phonon cat states entangled with the cavity Fock states. Measurement of the cavity photon number states produces phonon even and odd cat states. The information leakage effect of two photon states on the fidelity of cat states is calculated, it is shown that for low average phonon number this effect is negligible and decreases by increasing the two photon cavity state. The Lindblad equation is solved numerically to obtain the environmental effects on the fidelity of cat states.     

The effect of Protexin on prevention of ileocecal infection by Mycobacterium avium subspecies paratuberculosis in dairy calves

The objective of this study was to evaluate the effect of Protexin (Probiotics International Ltd., South Petherton, UK) in the prevention of ileocecal infection by Mycobacterium avium ssp. paratuberculosis (MAP) in dairy calves in the field situation. Forty Holstein bull calves whose dams were paratuberculosis negative (confirmed by serum ELISA test and fecal nested PCR) were randomly selected in 2 groups. All calves were fed raw milk collected from the bulk tank in a paratuberculosis-infected dairy farm, which was confirmed by PCR. The treatment group (20 calves) was given 2 g of Protexin from birth until weaning (90 d). The control group (20 calves) did not consume Protexin. The calves were culled at 12 mo of age and the ileocecal lymph nodes were sampled. The lymph nodes were tested by nested PCR to evaluate MAP infection. In the treatment group, 2 out of 20 calf (10%) ileoceca were infected by MAP, whereas in the control group, 8 out of 20 calf (40%) ileoceca were infected by MAP. A significant difference existed between ileocecal infection by MAP in treatment and control groups. Thus, Protexin showed a significant effect in decreasing the ileocecal infection by MAP.     

A local linear radial basis function neural network for financial time-series forecasting

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.