Study of macromolecular interactions in low‐fat brined cheese modified with Zedu gum

The functionality of Zedu gum as a fat mimetic in low‐fat brined cheese was studied. The physicochemical, textural, rheological, microstructural and sensory properties of cheese samples modified with 0.1% and 0.25% of Zedu gum were compared to those of control cheeses (low‐fat and full‐fat cheeses with no fat mimetic) during ripening. To obtain further information about the cheeses' structure and interactions between macromolecules (casein protein and Zedu gum), other parameters were analysed by differential scanning calorimetry and Fourier transform infrared (FTIR) spectroscopy. Incorporation of Zedu gum into low‐fat cheese caused an open microstructure and softer texture in comparison with the control low‐fat cheese. The thermal properties and FTIR spectra of the cheeses were influenced by both fat mimetic and ripening time. On days 1 and 60 of ripening time, the lower value of enthalpy of the low‐fat cheese with 0.25 g of Zedu gum/kg of milk (AS 0.25) in comparison with control low‐fat cheese could have been due to the electrostatic nature of the interactions between Zedu gum and casein protein. On both days, the FTIR spectrum of AS 0.25 showed a well separated absorption at 1746 cm^?1 possibly due to the formation of ester groups as a result of the interaction of the carbonyl groups in Zedu gum with the hydroxyl groups of some amino acids in casein.     

Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

Investigation of the Neutron Angular Distribution and Neutron Yield on the APF Plasma Focus Device

This paper presents an experimental study of neutron yield as well as neutron angular distribution on the APF plasma focus device. The system operates with pure deuterium gas in varying the filling pressure and working voltages of 11.5 and 13.5 kV. The maximum average of neutron yield is (2.88 ± 0.29 × 10⁸) neutrons per shot at the pressure of 7 torr. The neutron angular distribution is measured with housing an array of seven silver activation Geiger-Muller counters at the angles of 0°, ±30°, ±60°, and ±90° in a distance of 90 cm from the anode tip. The results of neutron angular distribution suggest that the neutron production mechanism may be predominantly beam target model.     

Subsurface structures detection by combining L-band polarimetricSAR and GPR data: example of the Pyla Dune (France)

The authors investigate the penetration capabilities of microwaves, particularly at L-band, for the mapping of subsurface heterogeneities such as lithology variations, moisture or sedimentary structures. The experiment site, the Pyla Dune, is a bare sandy area allowing high signal penetration and presenting large subsurface structures (paleosoils) at varying depths. Several radar data sets over this area are available. A polarimetric analysis of airborne synthetic aperture radar (SAR) data as web as the ground penetrating radar (GPR) sounding experiment show that subsurface scattering occurs at several places. The SAR penetration depth is estimated by inverting a scattering model for which the subsurface structure geometric and dielectric properties are determined by the GPR data analysis. These results suggest that airborne radar systems in a lower frequency range (P-band) should be able to detect subsurface moisture down to several meters, leading to innovative Earth observation systems for hydrogeology in arid regions     

Spatial heterogeneity enhances and modulates excitability in a mathematical model of the myometrium

The muscular layer of the uterus (myometrium) undergoes profound changes in global excitability prior to parturition. Here, a mathematical model of the myocyte network is developed to investigate the hypothesis that spatial heterogeneity is essential to the transition from local to global excitation which the myometrium undergoes just prior to birth. Each myometrial smooth muscle cell is represented by an element with FitzHugh–Nagumo dynamics. The cells are coupled through resistors that represent gap junctions. Spatial heterogeneity is introduced by means of stochastic variation in coupling strengths, with parameters derived from physiological data. Numerical simulations indicate that even modest increases in the heterogeneity of the system can amplify the ability of locally applied stimuli to elicit global excitation. Moreover, in networks driven by a pacemaker cell, global oscillations of excitation are impeded in fully connected and strongly coupled networks. The ability of a locally stimulated cell or pacemaker cell to excite the network is shown to be strongly dependent on the local spatial correlation structure of the couplings. In summary, spatial heterogeneity is a key factor in enhancing and modulating global excitability.     

Discrimination potential of X-band polarimetric SAR data

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and α-angle, show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same entropy/α-angle zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low.     

Accuracy of wet snow mapping using simulated Radarsat backscattering coefficients from observed snow cover characteristics

Wet snow cover mapping by means of airborne and spaceborne SAR is operational today and successfully applied in rugged high mountain terrain and in agricultural area. This paper proposes a numerical study to estimate the accuracy of wet snow mapping by using a radar backscattering model that simulates backscattering from a multi-layer snowpack for various snow cover conditions and for SAR parameters specific to Radarsat (C-HH). Field measurements carried out in numerous sites during the winters of 1994 to 1996 in several areas of Quebec (Canada) have allowed to choose some typical snow profiles and the corresponding snow/soil parameters. Results indicate that under the assumptions used in the model and the simulations, for the standard mode S1 of Radarsat (20 to 27.4) and in the case of wet snow cover with liquid water content of 1%, the optimum relative under-and over-estimation of wet snow pixels are of the order of 23.9% and 13.4%, respectively. For wet snow cover at 2%, the algorithm operates with a relative under-estimation of wet snow pixels around 8.5% and a relative over-estimation of the order of 1.7%. For wet snow with liquid water content of 4%, the relative under-and over-estimation of wet snow pixels is around 0.8% and 0.3%, respectively. They are negligible for wet snow with liquid water content higher than 4%. With the standard mode S7 of Radarsat (44.9 to 49.4), the wet snow mapping algorithm leads to a slightly lower performance than with the standard mode S1. The accuracy of the method for wet snow mapping demonstrates the high potential of SAR for snow monitoring. It is considered sufficient when the liquid water content of the snowpack is higher than 1% for actual snow conditions similar to those eight observed conditions used in this study.