Bunsen section thermodynamic model for hydrogen production by the sulfur–iodine cycle

A model for the Bunsen section of the Sulfur–Iodine thermo-chemical cycle is proposed, where sulfur dioxide reacts with excess water and iodine to produce two demixing liquid aqueous phases (H₂SO₄ rich and HI rich) in equilibrium. Considering the mild temperature and pressure conditions, the UNIQUAC activity coefficient model combined with Engels' solvation model is used. The complete model is discussed, with HI solvation by water and by iodine as well as H₂SO₄ solvation by water, leading to a very high complexity with almost hundred parameters to be estimated from experimental data. Taking into account the water excess, a successful reduced model with only 15 parameters is proposed after defining new apparent species. Acids total dissociation and total H⁺ solvation by water are the main assumptions. Results show a good agreement with published experimental data between 25 °C and 120 °C.     

Fatty acid composition of pinaceae as taxonomic markers

Following our previous review on Pinus spp. seed fatty acid (FA) compositions, we recapitulate here the seed FA compositions of Larix (larch), Picea (spruce), and Pseudotsuga (Douglas fir) spp. Numerous seed FA compositions not described earlier are included. Approximately 40% of all Picea taxa and one-third of Larix taxa have been analyzed so far for their seed FA compositions. Qualitatively, the seed FA compositions in the three genera studied here are the same as in Pinus spp., including in particular the same Δ5-olefinic acids. However, they display a considerably lower variability in Larix and Picea spp. than in Pinus spp. An assessment of geographical variations in the seed FA composition of P. abies was made, and intraspecific dissimilarities in this species were found to be of considerably smaller amplitude than interspecific dissimilarities among other Picea species. This observation supports the use of seed FA compositions as chemotaxonomic markers, as they practically do not depend on edaphic or climatic conditions. This also shows that Picea spp. are coherently united as a group by their seed FA compositions. This also holds for Larix spp. Despite a close resemblance between Picea and Larix spp. seed FA compositions, principal component analysis indicates that the minor differences in seed FA compositions between the two genera are sufficient to allow a clear-cut individualization of the two genera. In both cases, the main FA is linoleic acid (slightly less than one-half of total FA), followed by pinolenic (5,9,12-18:3) and oleic acids. A maximum of 34% of total Δ5-olefinic acids is reached in L. sibirica seeds, which appears to be the highest value found in Pinaceae seed FA. This apparent limit is discussed in terms of regio- and stereospecific distribution of Δ5-olefinic acids in seed triacylglycerols. Regarding the single species of Pseudotsuga analyzed so far (P. menziesii), its seed FA composition is quite distinct from that of the other two genera, and in particular, it contains 1.2% of 14-methylhexadecanoic (anteiso-17:0) acid. In the three genera studied here, as well as in most Pinus spp., the C₁₈Δ5-olefinic acids (5,9-18:2 and 5,9,12-18:3 acids) are present in considerably higher amounts than the C₂₀Δ5-olefinic acids (5,11-20:2 and 5,11,14-20:3 acids).     

Use of microfibrillated cellulose and dendritic copper for the elaboration of conductive films from water- and ethanol-based dispersions

In this study, microfibrillated cellulose (MFC) and dendritic copper were used as binder and conductive phase for the elaboration of self-standing conductive films and coatings. A filtration technique was used to prepare MFC/Cu films from particle dispersions in water and ethanol. In aqueous slurries copper oxidized and an additional corona treatment or the use of zinc particles as sacrificial anode were necessary to obtain films with conductivities ranging from 70 to 2500 S m^?1, respectively. In ethanol-based MFC/Cu slurries, copper was subjected to limited oxidation. However, the low packing density of conductive particles (below the percolation threshold) led to resistive films which, after densification by calendering, displayed extremely high conductivities, up to 70000 S m^?1. Aqueous MFC/Cu slurries were successfully used for the deposition of conductive coatings on copy paper by Mayer rod coating and screen printing, which were subsequently treated by corona discharge and calendering. The obtained coatings displayed intermediate conductivity (i.e. 95 and 570 S m^?1 for rod coating and screen printing, respectively), which can be further increased using zinc particles or ethanol-based formulations.     

Machine learning to design full-reference image quality assessment algorithm

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.     

Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture

This paper describes experimental results regarding the real time implementation of continuous time recurrent neural networks (CTRNN) and the dynamic back-propagation through time (BPTT) algorithm for the on-line learning control laws. Experiments are carried out to control the balance of a biped robot prototype in its standing posture. The neural controller is trained to compensate for external perturbations by controlling the torso’s joint motions. Algorithms are embedded in the real time electronic unit of the robot. On-line learning implementations are presented in detail. The results on learning behavior and control performance demonstrate the strength and the efficiency of the proposed approach.     

Effective strain rate sensitivity of two phase materials

Analytical expressions are derived for the effective strain rate sensitivity exponent of a two phase material when the behavior of both phases and of the composite itself can be described by power law relations between the stress and strain rates. The material is assumed to be plastically isotropic and obey to von-Mises type creep behavior. Two types of boundary conditions are considered: strain or stress-controlled. The obtained formulas are applied to a geological composite material (mixture of camphor and octachloropropane) with the help of different simple models of two phase composites.