Motion synthesis and editing for the generation of new sign language content

Machine Translation - Existing work on the animation of signing avatars often relies on pure procedural techniques or on the playback of Motion Capture (MoCap) data. While the first solution...     

Essential fatty acids in trout serum lipoproteins,vitellogenin and egg lipids

This paper describes evidence of (n−3) and particularly of 22∶6 (n−3) fatty acid enrichment in trout lipoproteins as well as in vitellogenin, egg lipovitellin and oil globule. Among the lipoproteins, HDL and LDL were the main forms of blood lipid transport, whereas phospholipids and cholesteryl esters are the preferential chemical carriers for (n−3) fatty acid transport. However, cholesteryl esters were less important as esterified fatty acid carriers than in man. Taken together with the data obtained in mammals, our results suggest that there may be a relationship between EFA activity and the distribution of the EFA among the lipoprotein lipid fractions in vertebrates, irrespective of the EFA series. Administration of an (n−3) fatty acid deficient diet for three months prior to trout spawning produced a significant increase in egg lipid content, primarily as a result of the increase of the oil globule composed almost exclusively of triacylglycerols. This diet decreased the 22∶6 (n−3), as well as the (n−3) fatty acid contents of lipoproteins, lipovitellin, vitellogenin and the oil globule. In contrast, the (n−3) fatty acid level was always higher in lipoproteins and lipovitellin than in the vitellogenin and the oil globule. Moreover, the relative levels of 22∶6 (n−3) and total (n−3) fatty acids were quite similar in lipoproteins and lipovitellin on the one hand, and in vitellogenin and the oil globule on the other. These findings suggest a direct relationship between the two forms of plasma lipid transport and the two egg compartments. During ovogenesis, dietary lipids seemed to be diverted from the adipose tissue and essentially deposited in the egg.     

Air impingement and intermittent drying: Application to apple and to mango

An original drying process combining air impingement and intermittent drying was studied on apple slices and mango cubes. The influence of four operating parameters (air velocity, drying/tempering periods, upper height, and air temperature) on the drying time and on the drying rate was evaluated. Continuous and intermittent drying were compared. The intermittency α = 1/7 (τ_on = 10 seconds and τ_off = 60 seconds) gave the best results. A time savings of 54% for apple and 67% for mango was reached. In continuous drying, a time savings of 4620 seconds was observed by increasing the air velocity from 6 to 40 m s^?1 for apple. Air temperatures of 328 K for apple and of 328 K or 338 K for mango were determined as optimum to prevent case‐hardening. Experimental results were fitted with the analytical solution of Fick's second law and the modified Page equation (average values R² = 0.985 and 0.961, for apple and mango, respectively). For both products, the apparent moisture diffusivity D_app, the drying constant k, the drying coefficient n, and the activation energy E_a, were identified. Activation energies calculated from the analytical solution were 30.3 and 36.8 kJ mol^?1 and were 25.4 and 30.0 kJ mol^?1 using the modified Page equation for apple and mango, respectively. Mango has an increased temperature sensitivity and thus will need less energy for drying than apple.     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Process optimization in the self-reacting friction stir welding of aluminum 6061-T6

Self-reacting friction stir welding (SR-FSW), also called bobbin-tool friction stir welding (BT-FSW), is a solid state welding process similar to friction stir welding (FSW) except that the tool has two opposing shoulders instead of the shoulder and a backing plate found in FSW. The tool configuration results in greater heat input and a symmetrical weld macrostructure. A significant amount of information has been published in the literature concerning traditional FSW while little has been published about SR-FSW. An optimization experiment was performed using a factorial design to evaluate the effect of process parameters on the weld temperature, surface and internal quality, and mechanical properties of self-reacting friction stir welded aluminum alloy 6061-T6 butt joints. The parameters evaluated were tool rotational speed, traverse speed, and tool plunge force. A correlation between weld temperature, defect formation (specifically galling and void formation), and mechanical properties was found. Optimum parameters were determined for the welding of 8-mm-thick 6061-T6 plate.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

Neural computing models for prediction of permeability coefficient of coarse-grained soils

Correlations are very significant from the earliest days; in some cases, it is essential as it is difficult to measure the amount directly, and in other cases it is desirable to ascertain the results with other tests through correlations. Soft computing techniques are now being used as alternate statistical tool, and new techniques such as artificial neural networks, fuzzy inference systems, genetic algorithms, and their hybrids were employed for developing the predictive models to estimate the needed parameters, in the recent years. Determination of permeability coefficient (k) of soils is very important for the definition of hydraulic conductivity and is difficult, expensive, time-consuming, and involves destructive tests. In this paper, use of some soft computing techniques such as ANNs (MLP, RBF, etc.) and ANFIS (adaptive neuro-fuzzy inference system) for prediction of permeability of coarse-grained soils was described and compared. As a result of this paper, it was obtained that the all constructed soft computing models exhibited high performance for predicting k. In order to predict the permeability coefficient, ANN models having three inputs, one output were applied successfully and exhibited reliable predictions. However, all four different algorithms of ANN have almost the same prediction capability, and accuracy of MLP was relatively higher than RBF models. The ANFIS model for prediction of permeability coefficient revealed the most reliable prediction when compared with the ANN models, and the use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in soil mechanics.     

Science and technology in the region: The output of regional science and technology, its strengths and its leading institutions

Summary We operationalize scientific output in a region by means of the number of articles (as in the SciSearch database) per year and technology output by means of the number of patent applications (as in the database of the European Patent Office) per priority year. All informetric analyses were done using the DIALOG online-system. The main research questions are the following: Which scientific and technological fields or topics are most influent within a region and which institutions or companies are mainly publishing articles or holding patents? Do the distributions of regional science and technology fields and of publishing institutions follow the well-known informetric function? Are there - as it is expected - only few fields and few institutions which dominate the region? Is there a connection between the economic power of a region and the regional publication and patent output? Examples studied in detail are seven German regions: Aachen, Düsseldorf, Hamburg, Köln (Cologne), Leipzig - Halle - Dessau, München (Munich), and Stuttgart. Three different indicators were used, science and technology attraction of a region (number of scientific articles and patents), science and technology intensity (articles and patents per 1,000 inhabitants), and science and technology density (articles and patents per 1 billion EURO gross value added). Top region concerning both attraction and intensity is Munich, concerning density it is Aachen.     

Electroseparation of an antibacterial peptide fraction from snow crab by-products hydrolysate by electrodialysis with ultrafiltration membranes

Recently, a snow crab by-products hydrolysate has demonstrated antibacterial properties due to a peptide with a molecular weight of about 800 Da, but only at high concentration. Consequently, peptide hydrolysate has been fractionated to obtain peptides in a more purified form. The aim of this work was to separate a snow crab by-products hydrolysate by electrodialysis with ultrafiltration membranes (EDUF). EDUF, which allows separation of molecules according to their charges and molecular weights, was used to recover and concentrate the active antibacterial fraction. Two different ultrafiltration membranes (20 and 50 kDa) and two electrical field strengths (2 and 14 V/cm) were used as separation parameters. After EDUF separation, the 300-600 Da peptide molecular weight range was the most recovered with an abundance of 94%. Moreover, fractionation at 14 V/cm with ultrafiltration membranes of 50 kDa allowed the recovery of an anionic fraction which showed antibacterial properties on Escherichia coli ATCC 25922 and Listeria innocua HPB 13.     

Efficient Photomodulation of Visible Eu(III) and Invisible Yb(III) Luminescences using DTE Photochromic Ligands for Optical Encryption

This work describes a class of complex combining three dithienylethene units and a lanthanide ion used as an optical system displaying a double encryption method: i) a colorful code, drawn and erased under UV and visible irradiations respectively, due to coloration and discoloration of the photochromic entities, and ii) a concomitant gradual disappearance and progressive restoration of the associated lanthanide ion luminescence triggered with the same stimuli. The innovation of the system stems from the emission color tunability, i.e., with either a lanthanide ion emitting only in the visible range (Eu³⁺) or with another lanthanide ion emitting only in the near infrared (NIR) range (Yb³⁺), therefore observable, or not, to the naked eye. This system is the very first one to achieve efficient repeatable modulation of pure NIR luminescence on photochemical command. Furthermore, it is proven to be highly efficient when embedded in a PDMS polymer opening real opportunities for practical applications as anti‐counterfeiting.