Different metabolic and absorption patterns of betaine in response to dietary intake of whole-wheat grain, wheat aleurone or rye aleurone in catheterized pigs

In the present study, a novel method of measuring the uptake of nutrients by 1H NMR spectroscopy was performed to determine dietary effects. Six pigs catheterized in the portal vein and mesenteric artery were fed breads made from whole-wheat grain (WWG), wheat aleurone flour (WAF; 19?% aleurone of bread) or rye aleurone flour (RAF; 15?% aleurone of bread) in a repeated 3?×?3 crossover design. Three meals were provided daily (at 0, 5 and 10?h), and each period comprised of 1?week. Portal and arterial blood samples were collected at fasting (?30?min) on day 4–7, and on day 7, pooled blood samples were collected at 0–2.5, 2.5–5, 5–7.5 and 7.5–10?h after first (0?h) daily meal. Using the arterial-venous difference and ANOVA–simultaneous component analysis, plasma betaine was found to accumulate during the experimental period. On day 7, plasma betaine concentrations was 20–40?% higher than when feeding the WAF diet compared with RAF and WWG (P?=?0.001), whereas the content of betaine in the WAF diet was about twice as high. The arterial-venous difference showed a tendency (P?=?0.078) for the diet–time interaction of betaine absorption in the absorption phases but there was no diet effect (P?=?0.31), which indicates a steady-state absorption has been reached on day 7. Furthermore, high plasma betaine levels correlated with low plasma creatine of endogenous origin. In conclusion, the explorative method revealed that the plasma concentration and absorption of betaine were differentially modified by the diets.     

Corner turn of SAR data based on multi-FPGAs parallel system

A segmented storage strategy is provided for corner turn of Synthetic Aperture Radar (SAR) data based on multiple Field-Programable Gate Arrays (multi-FPGAs) parallel system. The optimal segmented length is related to the type of the Double-Data-Rate (DDR) memory. Address mapping between pixel location and memory location is expressed in pseudo-code, and the address mapping between bus address and memory address is also deduced in universal expression. A hardware module is given to implement DDR2 SDRAM controller. Practical debugging and experiment have proved that the segmented storage method balances the access rate between row and column in memory cells and accelerates the corner turn of two dimensional image data. Compared with previous related works, our implementation could get higher Throughput/Area and provide much more optimal performance.     

Mechanisms and Kinetics of Liquid Silicon Oxidation During Industrial Refining

During oxidative ladle refining (OLR) of silicon, the metal surface is partly oxidized, resulting in the formation of a condensed silica fume (SiO₂). This fugitive emission of silica represents a potential health hazard to the workers in the silicon and ferrosilicon industry. In the current work, industrial measurement campaigns aimed at recording the fume generation during OLR were performed at the Elkem Salten plant in Norway. The measured amounts of silica produced were 2.5–5.1?kg/h, depending on the gas flow rate in the refining process. The rate of silica production correlates with the total flow rate and amount of air in the purge gas, and increases as the flow rate increases. The results of this work suggest that fume generation during OLR primarily results from oxidation of the exposed metal surface, with oxygen transport from the surrounding atmosphere to the metal surface being the limiting factor. Other identified mechanisms of SiO₂ formation were splashing of the metal and/or oxidation of SiO gas carried with the refining purge gas.     

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.     

Synchronization of stochastic Markovian jump neural networks with reaction-diffusion terms

In this paper, we address the synchronization problem of a class of stochastic Markovian jump reaction-diffusion neural networks with Dirichlet boundary conditions. By using the Lyapunov-Krasovskii functional method, feedback control approach, and stochastic analysis technique, the sufficient synchronization conditions including the information of reaction-diffusion terms are obtained, which are expressed as linear matrix inequalities (LMIs). Finally, the effectiveness of the developed methods is shown by simulation examples.