Determination of Arsenic in Honey,Propolis, Pollen,and Honey Bees by Microwave Digestion and Hydride Generation Flame Atomic Absorption

The toxic properties of arsenic are well known. Honey has been widely used for monitoring this element. The present work reports a novel method for the determination of arsenic in honey, bees, pollen, and propolis, based on the coupling of microwave digestion and hydride generation. Method development included the quantitative reduction of arsenic(V) to arsenic(III), the acid used for dilution, and the complete removal of the gases following digestion. The method performance was satisfactory with recoveries between 83% and 111% and corresponding relative standard deviations between 3.1% and 24%. Among the 32 samples of honey, propolis, pollen, and honey bees analyzed, arsenic was detected in four out of six propolis samples at the method limit of detection (0.4?µg?g^?1). The results indicate that propolis may be an efficient indicator for arsenic.     

Reduction of Jacobi manifolds via Dirac structures theory

We first recall some basic definitions and facts about Jacobi manifolds, generalized Lie bialgebroids, generalized Courant algebroids and Dirac structures. We establish an one-one correspondence between reducible Dirac structures of the generalized Lie bialgebroid of a Jacobi manifold (M,Λ,E) for which 1 is an admissible function and Jacobi quotient manifolds of M. We study Jacobi reductions from the point of view of Dirac structures theory and we present some examples and applications.     

Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption

This paper introduces a novel methodology for the global optimization of general constrained grey-box problems. A grey-box problem may contain a combination of black-box constraints and constraints with a known functional form. The novel features of this work include (i) the selection of initial samples through a subset selection optimization problem from a large number of faster low-fidelity model samples (when a low-fidelity model is available), (ii) the exploration of a diverse set of interpolating and non-interpolating functional forms for representing the objective function and each of the constraints, (iii) the global optimization of the parameter estimation of surrogate functions and the global optimization of the constrained grey-box formulation, and (iv) the updating of variable bounds based on a clustering technique. The performance of the algorithm is presented for a set of case studies representing an expensive non-linear algebraic partial differential equation simulation of a pressure swing adsorption system for \(\hbox {CO}_{2}\). We address three significant sources of variability and their effects on the consistency and reliability of the algorithm: (i) the initial sampling variability, (ii) the type of surrogate function, and (iii) global versus local optimization of the surrogate function parameter estimation and overall surrogate constrained grey-box problem. It is shown that globally optimizing the parameters in the parameter estimation model, and globally optimizing the constrained grey-box formulation has a significant impact on the performance. The effect of sampling variability is mitigated by a two-stage sampling approach which exploits information from reduced-order models. Finally, the proposed global optimization approach is compared to existing constrained derivative-free optimization algorithms.     

On twisted contact groupoids and on integration of twisted Jacobi manifolds

We introduce the concept of twisted contact groupoids, as an extension either of contact groupoids or of twisted symplectic ones, and we discuss the integration of twisted Jacobi manifolds by twisted contact groupoids. We also investigate the very close relationships which link homogeneous twisted Poisson manifolds with twisted Jacobi manifolds and homogeneous twisted symplectic groupoids with twisted contact ones. Some examples for each structure are presented.     

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very e     

Methodologies for determination of soil ionization gradient

In this paper the results of the research being carried out on the impulse characteristics of soils and more specifically the ionization voltage gradient are presented. The determination of the soil ionization voltage and the respective gradient using a universally accepted method is yet to be established. Aiming at meeting this need, the present paper critically presents these methods and records the ionization gradient values for soil types, which have derived from different experimental approaches. The comprehensive study of these methods is intended to serve as a guide for researchers studying the nonlinear phenomena developed in the soil.     

The peptide (Z)‐Pro–Leuol

The structure of the synthetic protected dipeptide (Z)‐Pro–Leuol [systematic name: benzyl 2‐(1‐hydroxy­methyl‐3‐methyl­butyl­amino­carbonyl)­pyrrolidine‐1‐carboxyl­ate], C₁₉H₂₈N₂O₄, was determined by X‐ray crystallography. The peptide adopts a novel backbone conformation compared with other longer oligopeptides containing Pro–Leuol.