Highly efficient synthesis of coumarin derivatives in the presence of H14[NaP5W30O110] as a green and reusable catalyst

A variety of heteropolyanions including: Keggin, Dawson, Preyssler, mixed addenda and sandwich types, catalyzed the formation of 4-methylnaphtho-(1,2-b)-pyran-2-one (coumarin) from the condensation of α-naphthol and ethylacetoacetate in a solvent free system and under heating conditions. Our data vividly indicate that sodium30–tungsto pentaphosphate, [NaP₅W₃₀O₁₁₀]¹⁴⁻, which so-called Preyssler’s anion, with high hydrolytic (pH 0–12) and thermal stability is the catalyst of choice. This catalyst catalyzed the synthesis of other coumarin derivatives in high yields and good selectivity.     

Highly efficient synthesis of benzopyranopyridines via ZrP2O7 nanoparticles catalyzed multicomponent reactions of salicylaldehydes with malononitrile and thiols

ZrP₂O₇ nanoparticles as an efficient catalyst have been used for the preparation of benzopyrano[2,3-b]pyridines from the four-component condensation reaction of salicylalde-hydes, thiols, and 2 equiv. of malononitrile under reflux conditions in ethanol in excellent yields and short reaction times.     

Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

In the present study, artificial neural network(ANN) approach was used to predict the stress–strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network(NN) to predict the stress–strain curve. In order to achieve this, three different NN architectures(feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network), three different transfer functions(purelin, Log-Sigmoid, and Tan-Sigmoid), number of hidden layers(1 and 2), number of neurons in the hidden layer(s),and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set(a,16 % a, 40 % a, and b stress–strain curves). After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress–strain curve of near b titanium alloy.     

Catalytic oxidative desulfurization of dibenzothiophene utilizing molybdenum and vanadium oxides supported on MCM-41

As a series of bimetallic nanocatalysts, molybdenum/vanadium oxides supported on the silica (MoO₃/V₂O₅/MCM-41) were prepared by the impregnation. Their catalytic activity in the oxidation of dibenzothiophene was investigated using H₂O₂ as an oxidant. Textures and surface properties of the prepared catalysts were characterized using FT-IR, XRD, FESEM-EDX and N₂ adsorption/desorption techniques. The effects of main process variables including H₂O₂/DBT molar ratio, catalyst dosage, reaction temperature and reaction time were analyzed by employing the response surface methodology (RSM). The effect of the MoO₃/V₂O₅ loading on the catalytic performance of the catalysts was also investigated. The results indicated that the catalytic activity of the catalyst was increased by enhancing the MoO₃/V₂O₅ content. Thus, the catalyst with high MoO₃/V₂O₅ loading (20%MoO₃/20%V₂O₅/60%MCM-41) indicated the highest catalytic activity and could convert 99.06% of dibenzothiophene under the optimum conditions. Mass and FT-IR analysis demonstrated that the major product of dibenzothiophene oxidation was its corresponding sulfone. The catalyst could be recycled five times without any considerable reduction in its catalytic activity. The kinetics of the reaction fitted the pseudo-first-order equation pretty well. Eventually, a reaction mechanism for the catalytic oxidation of DBT in the presence of MoO₃/V₂O₅/MCM-41 was proposed.     

A Constraint-Elimination Technique for Linearly Constrained Array Processing

A new technique is presented for the problem of linearly constrained multi-channel-array processing. The purpose of the array processing is to reject undesired noise, in the minimum-mean-squared-error sense, while responding to any signal coming from a particular direction with a preset, constrained frequency response. In the constraint-elimination technique proposed in this paper, the original constrained-array-processing problem is transformed to the simple unconstrained-Wiener-filtering problem. Transformation of the problem is achieved by introducing a ?compensating? channel to the multichannel processor. Input to the compensating channel is the channel-averaged input. The constraint is then eliminated by expressing the filter weights of the compensating channel in terms of those of the original processor. An adaptive algorithm is derived by applying the stochastic approximation to the simplified Wiener-filtering problem. Simulation experiments verify that the constrained-array-processing problem is properly transformed to the unconstrained one by the proposed technique. Additional experiments show that the adaptive algorithm generally reduces output power while maintaining the constrained frequency response to the desired signal.     

A CUSUM-based method for monitoring simple linear profiles

In most statistical process control applications, the quality of a process or product is characterized by univariate or multivariate quality characteristics and monitored by the corresponding univariate and multivariate control charts, respectively. However, sometimes, the quality of a process or a product is better characterized by a relationship between a response variable and one or more explanatory variables. This relationship, which can be linear, nonlinear, or even a complicated model, is referred to as a profile. So far, several methods have been proposed for monitoring simple linear profiles. In this paper, a new method based on cumulative sum statistics is proposed to enhance monitoring of linear profiles in phase II. The performance of the proposed method is evaluated by average run length criterion. A comprehensive comparison is also conducted between the performance of the proposed method and the existing methods for monitoring simple linear profiles. The results show that the proposed method performs satisfactorily. In addition, the effects of reference value, sample size, and corrected sum of squares of explanatory variables on the performance of the proposed method are investigated.     

Evaluation of antioxidant activities of Iranian sumac (R. coriaria L.) fruit and spice extracts with different solvents

The antioxidant capacity and phenolic content of three sumac (Rhus coriaria L.) samples, including brown sumac fruit, brown sumac powder and red sumac powder were investigated in the present study. Methanol, ethanol, distilled water, and a mixture of methanol and ethanol (1:1) were used as solvent systems. Antioxidant activities of extracts were screened using 1,1-diphenyl-2-picrylhydrazyl DPPH radical scavenging method. Phenolic content was determined through Folin–Ciocaltaeu procedure. The efficiency of the solvents used to extract phenols from the samples varied considerably. The phenolic content of brown sumac powder ranged from 2.906 to 2.997 gallic acid equivalents/100 g (GAE/100 g), while that of the brown sumac fruit was 2.438–2.529 GAE/100 g, and that of the red sumac powder was 2.172–2.263 GAE/100 g. Findings indicated that water extracts of sumac have effective antioxidant and radical scavenging activities as compared to other extracts.     

A Proteomic Analysis of Human Follicular Fluid: Comparison between Younger and Older Women with Normal FSH Levels

The follicular fluid (FF) is produced during folliculogenesis and contains a variety of proteins that play important roles in follicle development and oocyte maturation. Age-related infertility is usually considered as a problem that can be solved by assisted reproduction technology. Therefore, the identification of novel biomarkers that are linked to reproductive aging is the subject of this study. FF was obtained from healthy younger (20–32 years old) and older (38–42 years old) women undergoing intracytoplasmic sperm injection (ICSI) due to male factor infertility. The FF was analyzed by two-dimensional gel electrophoresis (2-DE). The power of two-dimensional gel electrophoresis and the identification of proteins were exploited using matrix-assisted laser desorption-ionization time-of-flight/time-of-flight (MALDI-TOF-TOF) mass spectrometry. Twenty three protein spots showed reproducible and significant changes in the aged compared to the young group. Of these, 19 protein spots could be identified using MALDI-TOF-TOF-MS. As a result of MASCOT search, five unique downregulated proteins were identified in the older group. These were identified as serotransferrin, hemopexin precursor, complement C3, C4 and kininogen. A number of protein markers were found that may help develop diagnostic methods of infertility.