Abstract: Gielis curves (GC) can represent a wide range of shapes and patterns ranging from star shapes to symmetric and asymmetric polygons, and even self intersecting curves. Such patterns appear in natural objects or phenomena, such as flowers, crystals, pollen structures, animals, or even wave propagation. Gielis curves and surfaces are an extension of Lamé curves and surfaces (superquadrics) which have benefited in the last two decades of extensive researches to retrieve their parameters from various data types, such as range images, 2D and 3D point clouds, etc. Unfortunately, the most efficient techniques for superquadrics recovery, based on deterministic methods, cannot directly be adapted to Gielis curves. Indeed, the different nature of their parameters forbids the use of a unified gradient descent approach, which requires initial pre-processings, such as the symmetry detection, and a reliable pose and scale estimation. Furthermore, even the most recent algorithms in the literature remain extremely sensitive to initialization and often fall into local minima in the presence of large missing data. We present a simple evolutionary algorithm which overcomes most of these issues and unifies all of the required operations into a single though efficient approach. The key ideas in this paper are the replacement of the potential fields used for the cost function (closed form) by the shortest Euclidean distance (SED, iterative approach), the construction of cost functions which minimize the shortest distance as well as the curve length using R-functions, and slight modifications of the evolutionary operators. We show that the proposed cost function based on SED and R-function offers the best compromise in terms of accuracy, robustness to noise, and missing data.

Abstract: Stimulus-responsive shape-memory materials have attracted tremendous research interests recently, with much effort focused on improving their mechanical actuation. Driven by the needs of nanoelectromechanical devices, materials with large mechanical strain, particularly at nanoscale level, are therefore desired. Here we report on the discovery of a large shape-memory effect in bismuth ferrite at the nanoscale. A maximum strain of up to ~14% and a large volumetric work density of ~600±90 J cm−3 can be achieved in association with a martensitic-like phase transformation. With a single step, control of the phase transformation by thermal activation or electric field has been reversibly achieved without the assistance of external recovery stress. Although aspects such as hysteresis, microcracking and so on have to be taken into consideration for real devices, the large shape-memory effect in this oxide surpasses most alloys and, therefore, demonstrates itself as an extraordinary material for potential use in state-of-art nanosystems.

Abstract: A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma (TM), GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: An electrochemical matairesinol biosensor was fabricated by immobilizing tyrosinase on a poly(thionine)/nafion/multi-walled carbon nanotube composite film. A polymeric film of the redox dye thionine enables the stable immobilization of tyrosinase while acting as a mediator for the enzymatic process has been incorporated into the carbon nanotube/nafion composite film. The immobilization method is based on crosslinking of the tyrosinase layer with an electropolymerized film of poly(thionine). The good homogenization of the electron conductor CNTs in the integrated films provides the possibility of a three-dimensional electron conductive network. The biosensor was characterized by electrochemical impedance spectroscopy and electrochemical characterization. The composite electrode exhibits catalytic activity, high sensitivity, stability and is applicable over a wide range of concentrations from 180 nM to 4.33 μM with a detection limit (LOD) of 37 nM. The obtained results suggest that the developed sensor can be successfully used for the determination of phenolic endocrine disruptors over a concentration range covering their environmental levels.