Molybdenum Imido Alkylidene Metathesis Catalysts that Contain Electron Withdrawing Biphenolates or Binaphtholates

We have prepared new Mo(NR)(CHCMe(2)Ph)(diolate) complexes (R = 2,6-i-Pr(2)C(6)H(3), 2,6-Me(2)C(6)H(3), 1-Adamantyl, or 2-CF(3)C(6)H(4)) that contain relatively electron-withdrawing binaphtholate (3,3'-bis-(9-anthracenyl), 3,3'-bispentafluorophenyl, or 3,3'-bis(3,5-bis(trifluoromethyl)phenyl) or biphenolate (3,3'-di-tert-butyl-5,5'-bistrifluoromethyl-6,6'-dimethyl-1,1'-biphenyl-2,2'-diolate) ligands. We also have prepared new monomeric Mo(NR)(CHCMe(2)Ph)(2,5-dimethylpyrrolide)(2) complexes and have treated them with biphenols or binaphthols in order to prepare several Mo(NR)(CHCMe(2)Ph)(diolate) species. In one case the new Mo(NR)(CHCMe(2)Ph)(diolate) complexes could be prepared only through reaction of a binaphthol [3,3'-bis(pentafluorophenyl)binaphthol] with a bis(2,5-dimethylpyrrolide) complex. The pyrrolide approach can be employed either to isolate catalysts on a preparative scale or to generate catalysts in situ. Several simple preliminary ring-closing metathesis reactions show that the new complexes are catalytically competent.     

Increased sensibility of mixed ferrite humidity sensors by subsequent heat treatment

The study presents the effects of different calcination temperatures on the size and morphology of mixed Co‐Ni ferrites, and the respective changes in the magnetic and electrical properties, with emphasis on their applicability as humidity sensors. The data suggests that the heat treatment does not significantly change the magnetic properties, but significant variations on the morphology of the nanoparticles, correlated with large changes in the electrical properties of the nanoparticles in terms of electrical permittivity and resistivity with electrical field frequency and humidity, with a clear influence of the calcination temperature over the sensibility of the materials to humidity.     

Separation and identification of amino acids from lignite humic acids by thin layer chromatography

Thin layer chromatography with and without temperature gradient was used to identify ten amino acids in the humic acid hydrolysate of Rovinari lignite, using cellulose and volcanic tuff as stationary phases. The acids found were L-leucine, isoleucine, phenylalanine, L-valine, tyrosine, proline, L-alanine, glutamic acid, threonine and L-lysine.     

Maximization of a nuclear system availability through maintenance scheduling optimization using a genetic algorithm

In this paper, a method for preventive maintenance scheduling optimization of standby systems, based on genetic algorithm and probabilistic safety analysis, is described. The goal of this approach is to improve the average availability of the system through the optimization of the preventive maintenance policy. Here, the genetic modeling propitiates unconstrained optimization, allowing nonconstant intervals between maintenance, adapting them to the aging parameter of the Weibull distribution used. In order to demonstrate the effectiveness of the proposed method, it is applied to a nuclear system of a two-loop pressurized water reactor. The results, when compared with those obtained by some standard maintenance policies, reveal gains at safety level.     

Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature

The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.     

Transfer functions with quasiCheby?shev-type characteristics obtained by a Darlington-derived transformation

The letter establishes the classes of transfer functions corresponding to amplitude characteristics of the Cheby?shev type as well as to transit-time characteristics of the quasiCheby?shev type, starting from the transform given in a previous letter.     

A software defined radio modeling framework for enabling cognitive applications

The rapid advances in software defined radio (SDR) technologies over the past decade encouraged their transition from the lab to field deployment, and shifted the usage from disparate communication devices to the creation of large resilient wireless networks capable of adapting to highly variable environment conditions in a cognitive manner. Simulation models capable of accurately representing the behavior and properties of SDR devices operating in a networked environment thus become a prerequisite for both the evaluation of such networks as well as for providing a development platform for creating new cognitive capabilities. This paper describes our work in creating a network simulation model framework for software defined radios that takes into account some of the unique behaviors and requirements of SDRs not previously seen in purely hardware devices. Factors such as large and variable communication delays between software modules, contention to shared resources, such as CPU or memory, as well as continuous tuning and environment awareness functionality essential to SDR demand a different modeling approach as well as novel techniques that enable accurate scale testing.     

Saddle point least squares preconditioning of mixed methods

We present a simple way to discretize and precondition mixed variational formulations. Our theory connects with, and takes advantage of, the classical theory of symmetric saddle point problems and the theory of preconditioning symmetric positive definite operators. Efficient iterative processes for solving the discrete mixed formulations are proposed and choices for discrete spaces that are always compatible are provided. For the proposed discrete spaces and solvers, a basis is needed only for the test spaces and assembly of a global saddle point system is avoided. We prove sharp approximation properties for the discretization and iteration errors and also provide a sharp estimate for the convergence rate of the proposed algorithm in terms of the condition number of the elliptic preconditioner and the discrete $inf?sup$ and $sup?sup$ constants of the pair of discrete spaces.