A Newton type iterative inverse method for heat-conduction problems

An inverse problem for identification of the coefficient in heat-conduction equation is considered. After reducing the problem to a nonlinear ill-posed operator equation, Newton type iterative methods are considered. The implicit iterative method is applied to the linearized Newton equation, and the key step in the process is that a new reasonable a posteriori stopping rule for the inner iteration is presented. Numerical experiments for the new method as well as for Tikhonov method and Bakushikskii method are given, and these results show the obvious advantages of the new method over the other ones.     

Singular nature of nonlinear macroscale effects in high-rate flow through porous mediaNature singulière des effets macroscopiques non linéaires dans un écoulement à hautes vitesses en milieux poreux

The quadratic law of laminar flow through porous media at high Reynolds numbers, which is well confirmed by the multiple experimental data, is shown to give rise to three fundamental paradoxes. All them can be resolved by assuming the singular structure of flow. The singularity is produced by the formation of jet brunches which invade the stagnant zones and sharply loss their kinetic energy. The numerical simulation confirms this effect. To cite this article: M. Panfilov et al., C. R. Mecanique 331 (2003).     

Guest-dependent complexation of triptycene-derived macrotricyclic host containing one anthracene moiety with paraquat derivatives: construction of [2]rotaxanes

Complexation between the triptycene-derived macrotricyclic polyether containing an anthracene unit and paraquat derivatives in both solution and solid state was investigated. It was found that the macrotricyclic host with multi-cavity structure could form a series of [2]pseudorotaxanes with different terminal functionalised paraquat derivatives in different threading modes, which subsequently resulted in the construction of two novel [2]rotaxanes.     

Structural,optical and magnetic properties of chromium and manganese co-doped SnO2 nanoparticles

The rutile phase Sn_0.99-xMn_xCr_0.01O₂ (x = 0.00, 0.01, 0.03, 0.05 and 0.07) nanoparticles were synthesized by facile chemical co-precipitation method using poly ethylene glycol (PEG) as a capping agent. The samples were characterized by EDAX to confirm the expected stoichiometry. The X-ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) spectroscopy analyses of these samples showed the formation of impurity free crystals with a single phase rutile type tetragonal crystal structure as that of (P4₂/mnm) of SnO₂. Optical absorption spectra and corresponding Tauc's plots showed a redshift of the absorption edge in SnO₂ after being co-doped with Cr and Mn. The samples were examined for its magnetic property using vibrating sample magnetometer which indicated that transition of magnetic signals from ferromagnetic to the paramagnetic nature with inclusion of Mn content in SnO₂: Cr host matrix. The observed magnetic behavior is well supported with the bound magnetic polarons (BMPs) model.     

Capacitance scaling of grain boundaries with colossal permittivity of CaCu3Ti4O12-based materials

Samples of copper-deficient CaCu₃Ti₄O₁₂ (CCTO) compared to the nominal composition, all synthesized via organic gel-assisted citrate process, show huge change of grain boundaries capacitance as deduced from a fit of an RC element model to the impedance spectroscopic data. The grain boundary capacitance is found to scale with the permittivity measured at 1 kHz weighted by the size of the grains. This result is found consistent with the internal barrier layer capacitance (IBLC) model.     

Natural and artificial light-harvesting systems utilizing the functions of carotenoids

Carotenoids are essential pigments in natural photosynthesis. They absorb in the blue–green region of the solar spectrum and transfer the absorbed energy to (bacterio-)chlorophylls, and so expand the wavelength range of light that is able to drive photosynthesis. This process is an example of singlet–singlet energy transfer and so carotenoids serve to enhance the overall efficiency of photosynthetic light reactions. Carotenoids also act to protect photosynthetic organisms from the harmful effects of excess exposure to light. In this case, triplet–triplet energy transfer from (bacterio-)chlorophyll to carotenoid plays a key role in this photoprotective reaction. In the light-harvesting pigment–protein complexes from purple photosynthetic bacteria and chlorophytes, carotenoids have an additional role, namely the structural stabilization of those complexes. In this article we review what is currently known about how carotenoids discharge these functions. The molecular architecture of photosynthetic systems will be outlined to provide a basis from which to describe the photochemistry of carotenoids, which underlies most of their important functions in photosynthesis. Then, the possibility to utilize the functions of carotenoids in artificial photosynthetic light-harvesting systems will be discussed. Some examples of the model systems are introduced.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.