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.     

Nickel‐Catalyzed Reductive Benzylation of Aldehydes with Benzyl Halides and Pseudohalides

The reductive benzylation of aromatic and aliphatic aldehydes with benzylic halides is reported using a nickel/zinc catalyst system. In addition to benzylic halides, the first report on the addition of benzylic triflates, acetates, tosylates and tritylates to aldehydes is also presented. By this new method a range of alcohols was synthesized efficiently from aldehydes and benzylic substrates at room temperature in moderate to high yields. The mild reaction conditions and good functional group tolerance make this nickel‐catalyzed process synthetically useful for the synthesis of diverse benzylic alcohols.

     

Effect of ionic strength on the reactivity ratios of acrylamide/acrylic acid (sodium acrylate) copolymerization

The ionic strength (IS) of polyelectrolyte solutions plays an important role in influencing reaction kinetics. The largely unstudied effect of IS on monomer reactivity ratios and copolymerization rates of acrylamide (AAm) and acrylic acid (AAc), in the form of sodium acrylate (NaAc), is investigated. Salt addition affects the nature of overall charges of the polyelectrolyte solution and diminishes the electrostatic repulsions between reacting chains. Therefore, changing the IS of the solution by incorporating salts affect not only the point estimates of the monomer reactivity ratios but also the overall behavior of the copolymerization (with a transition to azeotropic behavior). Experimental results on copolymerization rates confirm the observed trends in reactivity ratio behavior. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40949.     

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.