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.     

Genomewide search for dehydrated hereditary stomatocytosis (hereditary xerocytosis): mapping of locus to chromosome 16 (16q23-qter)

Dehydrated hereditary stomatocytosis, also known as "hereditary xerocytosis," is caused by a red blood cell-membrane defect characterized by stomatocytic morphology, increased mean corpuscular hemoglobin concentration, decreased osmotic fragility, increased permeability to the univalent cations Na+ and K+, and an increased proportion of phosphatidylcholine in the membrane. The clinical presentation is heterogeneous, ranging from mild to moderate hemolytic anemia associated with scleral icterus, splenomegaly, and choletithiasis. Iron overload may develop later in life. The disease is transmitted as an autosomal dominant trait. We recruited a large three-generation Irish family affected with DHS and comprising 23 members, of whom 14 were affected and 9 were healthy. Two additional, small families also were included in the study. The DNA samples from the family members were used in a genomewide search to identify, by linkage analysis, the DHS locus. After the exclusion of a portion of the human genome, we obtained conclusive evidence for linkage of DHS to microsatellite markers on the long arm of chromosome 16 (16q23-q24). A maximum two-point LOD score of 6.62 at recombination fraction .00 was obtained with marker D16S520. There are no recombination events defining the telomeric limit of the region, which therefore is quite large. No candidate genes map to this area.     

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.     

Experimental setup for lensless imaging via soft x-ray resonant scattering

We have developed a setup for measuring holographically formed interference patterns using an integrated sample-mask design. The direct space image of the sample is obtained via a two-dimensional Fourier transform of the X-ray diffraction pattern. We present the details of our setup, commenting on the influence of geometrical parameters on the imaging capabilities. As an example, we present and discuss the results of test experiments on a patterned Co film.     

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.