Dynamic correlation function for the propagation of light in disordered medium

We have calculated the time autocorrelation function of the intensity of light multiply scattered in the medium with dynamical properties. A new asimptotic for larget is found.     

A highly sensitive RP-HPLC-fluorescence method to study aldehyde oxidase activity

Aldehyde oxidase is a widely distributed enzyme that is involved in the metabolism of an extensive range of aldehydes and N-heterocyclic compounds with physiological, pharmacological, and toxicological relevance. In the present study, a highly sensitive RP-HPLC-fluorescence method based on the oxidation of phenanthridine to phenanthridinone has been developed and validated to assay aldehyde oxidase activity in biological samples. Determination of phenanthridinone was achieved on a C18 column using 10 mmol/L phosphate buffer (pH 5.0) containing 0.1 mmol/L EDTA-acetonitrile (40 + 60, v/v) as the mobile phase. The fluorescence intensity of phenanthridinone was measured at 364 nm with excitation at 236 nm. The proposed method was precise, accurate, specific and rapid (analysis time, approximately 8 min) with a mean RSD of 2.54%. Peak responses were linear from 0.5 to 100 nmol/L, with an LOD of 0.125 nmol/L. The applicability of the method was demonstrated by measurement of aldehyde oxidase activity in rat liver, kidney, ovary, and heart fractions.     

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.     

Application of biosynthesized palladium nanoparticles (Pd NPs) on Rosa canina fruit extract‐modified graphene oxide as heterogeneous nanocatalyst for cyanation of aryl halides

A green palladium (Pd)‐based catalyst supported on Rosa canina fruit extract‐modified graphene oxide [Pd nanoparticles (NPs)/reduced graphene oxide (RGO)‐Rosa canina] hybrid materials has been used as a recoverable and heterogeneous nanocatalyst for cyanating aryl halides using K₄[Fe (CN)₆] as the resource of cyanide. The nitriles were achieved in good to high yield, and the catalyst can be recovered and reused for up to seven cycles with no remarkable decrease in its catalytic activity.     

Synthesis and Application of Graphene Oxide-supported Aluminium Chloride as a Novel and an Environmentally Friendly Catalyst for the Preparation of Bis(pyrazolyl)methanes

Russian Journal of Organic Chemistry - The immobilization of AlCl3, an extremely hygroscopic Lewis acid, on graphene oxide as a support material with a high surface area was studied. The obtained...     

Computation of V-notch shape factors in four-point bend specimen for fracture tests on brittle materials

Four-point bend (FPB) specimen is an important test sample in mixed mode fracture study of notched components made from brittle materials like rocks, brittle polymers, ceramics, etc. On the other hand, the notch stress intensity factors (NSIFs) are vital parameters in brittle fracture assessment of V-notched structures. Therefore, computation of NSIFs in FPB specimens is of practical interest to engineers and researchers. Since the available methods for calculating the NSIFs are often cumbersome and need complicated calculations, it is preferred to show them as a set of dimensionless parameters for the FPB specimen. In this research, the finite element method coupled with a recently developed algorithm called FEOD is employed to calculate the NSIFs of a FPB specimen for several V-shape notches and for different combinations of mode I and mode II. The obtained NSIFs are then converted to dimensionless parameters called notch shape factors and are illustrated in a number of figures. It is shown that depending on the notch depth and the location of loading points, full mode mixity from pure mode I to pure mode II can be provided in the FPB specimen. The numerical results obtained in this research are verified by using very limited results reported earlier in literature.     

Implementation of dynamic programming for chaos control in discrete systems

In this paper the control of discrete chaotic systems by designing linear feedback controllers is presented. The linear feedback control problem for nonlinear systems has been formulated under the viewpoint of dynamic programming. For suppressing chaos with minimum control effort, the system is stabilized on its first order unstable fixed point (UFP). The presented method also could be employed to make any desired nth order fixed point of the system, stable. Two different methods for higher order UFPs stabilization are suggested. Afterwards, these methods are applied to two well-known chaotic discrete systems: the Logistic and the Henon Maps. For each of them, the first and second UFPs in their chaotic regions are stabilized and simulation results are provided for the demonstration of performance.     

Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network

Classification of experimental datasets such as target and clutter in sonar applications is a complex and challenging problem. One of the most useful instrument to classify sonar datasets is Multi-Layer Perceptron Neural Network (MLP NN). In this paper, due to the optimally updating the weights and biases vector of the MLP NN, Biogeography-Based Optimization (BBO) is used to train the network. BBO has a fair ability to solve high-dimensional real-world problems (such as sonar dataset classification) by maintaining a suitable balance between exploration and exploitation phases. The performance of BBO is sensitive to the migration model, especially for high-dimensional problems. To improve the exploitation ability of BBO and to record the better results for classifying sonar dataset, we propose novel migration models such as exponential-logarithmic, and some improved migration models having different emigration and immigration mathematical functions. To validate the performance of the proposed classifiers, this network will classify three datasets with various sizes and complexities. The simulation results indicate that our newly proposed classifiers perform better than the other benchmark algorithms in addition to original BBO in terms of avoiding gets stuck in local minima, classification accuracy, and convergence speed.     

Chaos control in lateral oscillations of spinning disks via nonlinear feedback

In this paper the problem of chaos control in single mode lateral oscillations of spinning disks is studied. At first, using the harmonic balance method, one of the periodic orbits of system is evaluated. Then proposing a nonlinear feedback strategy a control law is presented for chaos elimination by tracking the mentioned periodic solution. It is shown that although the system is not input-state feedback linearizable, by defining an output signal and using the input–output linearization method, the objective of complete periodic orbit tracking is achieved. The sufficient condition for this purpose is presented, and the performance of proposed method is examined by numerical simulation.