Improving light absorption in organic solar cells by plasmonic contribution

Plasmonic phenomenon inside the materials composing an organic solar cell based on a photoactive poly(2-methoxy-5-(2′-ethyl-hexyloxy)-1,4-phenylenevinylene):(6,6)-phenyl-C₆₁-butyric-acid-methyl ester (MEH-PPV:PCBM) bulk heterojunction is studied using Finite Difference Time Domain (FDTD) method calculations and the modeling results are compared with experimental results.Enhanced absorptance of light up to 50% is experimentally obtained in a 50-nm-thick blend layer including spin-coated silver nanospheres with a diameter of 40 nm. FDTD calculations based on the design of 2D-grating of nanoparticles confirm the high values of absorptance. Spatial distributions of electromagnetic field power density in the structures show confinement of the power at the interface or in the vicinity of the nanoparticles depending on the wavelength and on the preferential directions.     

Stability Is Not Everything: The Case of the Cyclisation of a Thrombin-Binding Aptamer

With the aim of developing a new approach to obtain improved aptamers, a cyclic thrombin-binding aptamer (TBA) analogue (cycTBA) has been prepared by exploiting a copper(I)-assisted azide–alkyne cycloaddition. The markedly increased serum resistance and exceptional thermal stability of the G-quadruplex versus TBA were associated with halved thrombin inhibition, which suggested that some flexibility in the TBA structure was necessary for protein recognition.     

Competition through selective inhibitory synchrony

Models of cortical neuronal circuits commonly depend on inhibitory feedback to control gain, provide signal normalization, and selectively amplify signals using winner-take-all (WTA) dynamics. Such models generally assume that excitatory and inhibitory neurons are able to interact easily because their axons and dendrites are colocalized in the same small volume. However, quantitative neuroanatomical studies of the dimensions of axonal and dendritic trees of neurons in the neocortex show that this colocalization assumption is not valid. In this letter, we describe a simple modification to the WTA circuit design that permits the effects of distributed inhibitory neurons to be coupled through synchronization, and so allows a single WTA to be distributed widely in cortical space, well beyond the arborization of any single inhibitory neuron and even across different cortical areas. We prove by nonlinear contraction analysis and demonstrate by simulation that distributed WTA subsystems combined by such inhibitory synchrony are inherently stable. We show analytically that synchronization is substantially faster than winner selection. This circuit mechanism allows networks of independent WTAs to fully or partially compete with other.     

Fault detection and isolation in manufacturing systems with an identified discrete event model

In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.     

An efficient algorithm for Horn description

We give a new algorithm for computing a prepositional Horn CNF formula given the set of its models. Its running time is O(|R|n(|R|+n)), where |R| is the number of models and n that of variables, and the computed CNF contains at most |R|n clauses. This algorithm also uses the well-known closure property of Horn relations in a new manner.     

Recognition of Simple Enlarged Horn Formulas and Simple Extended Horn Formulas

The enlarged Horn formulas generalize the extended Horn formulas introduced by Chandru and Hooker (1991). Their satisfying truth assignments can be generated with polynomial delay. Unfortunately no polynomial algorithm is known for recognizing enlarged Horn formulas or extended Horn formulas. In this paper we define the class of simple enlarged Horn formulas, a subclass of the enlarged Horn formulas, that contains the simple extended Horn formulas introduced by Swaminathan and Wagner (1995). We present recognition algorithms for the simple enlarged Horn formulas and the simple extended Horn formulas whose complexity is bounded by the complexity of the arborescence-realization problem.     

Helicoidal vortex model for steady and unsteady flows

A helicoidal vortex model is used to predict the flow past the blades of a wind turbine. As the tip speed ratio (TSR) varies, the environment in which the blades operate varies, and for low enough TSR, the local angle of attack α will be larger than (α)_Clmax, the incidence of maximum lift. The problem becomes highly nonlinear and it is shown that adding an artificial viscosity term to the equation allows the iterative algorithm to converge toward a smooth solution that is physically acceptable.The introduction of unsteady effects is useful to understand the cyclic forces and moments due to yaw or tower interaction, both for the design of blades to account for fatigue and for power output prediction. The 2-D impulsively plunging plate problem is solved with a semi-implicit scheme and the integral and numerical solutions compared and shown to be in excellent agreement. A 2-D test case to study the convection of a periodic shedding of vorticity downstream of a blade element is analyzed using the same semi-implicit algorithm and a stretched mesh similar to that used to model a turbine vortex sheet. The scheme captures accurately the vorticity distribution in the wake. Finally, the scheme is applied to the simulation of the NREL two-bladed rotor in yaw to assess the validity of the approach.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.