Design of a cost-effective on-grid hybrid wind–hydrogen based CHP system using a modified heuristic approach

An economic model and optimization procedure is developed in this paper for grid-connected hybrid wind–hydrogen combined heat and power systems for residential applications in northeastern Iran. The model considers various significant factors: energy production cost, electrical trade with local grid, electrical power generation from the wind/hydrogen energy system, thermal recovery from the fuel cell, and maintenance. Also, various tariffs for purchasing and selling electrical energy from the local grid are considered for the hybrid system operation. The optimization objective is to minimize the system total cost subject to relevant constraints for residential applications. To achieve this aim, an efficient optimization method is proposed based on particle swarm optimization. The proposed algorithm performance is compared with that for the imperialist competition algorithm. The results show that the hybrid system is the most cost-effective for the residential load, and the results of the proposed algorithm are more promising than those for the alternative algorithm.     

Inverse material identification in coupled acoustic-structure interaction using a modified error in constitutive equation functional

This work focuses on the identification of heterogeneous linear elastic moduli in the context of frequency-domain, coupled acoustic-structure interaction (ASI), using either solid displacement or fluid pressure measurement data. The approach postulates the inverse problem as an optimization problem where the solution is obtained by minimizing a modified error in constitutive equation (MECE) functional. The latter measures the discrepancy in the constitutive equations that connect kinematically admissible strains and dynamically admissible stresses, while incorporating the measurement data as additional quadratic error terms. We demonstrate two strategies for selecting the MECE weighting coefficient to produce regularized solutions to the ill-posed identification problem: 1) the discrepancy principle of Morozov, and 2) an error-balance approach that selects the weight parameter as the minimizer of another functional involving the ECE and the data misfit. Numerical results demonstrate that the proposed methodology can successfully recover elastic parameters in 2D and 3D ASI systems from response measurements taken in either the solid or fluid subdomains. Furthermore, both regularization strategies are shown to produce accurate reconstructions when the measurement data is polluted with noise. The discrepancy principle is shown to produce nearly optimal solutions, while the error-balance approach, although not optimal, remains effective and does not need a priori information on the noise level.     

Survey of 1012 moldy dwellings by culture fungal analysis: Threshold proposal for asthmatic patient management

Different countries have tried to define guidelines to quantify what levels of fungi are considered as inappropriate for housing. This retrospective study analyzes indoor fungi by cultures of airborne samples from 1012 dwellings. Altogether, 908 patients suffering from rhinitis, conjunctivitis, and asthma were compared to 104 controls free of allergies. Portuguese decree law no 118/2013 (PDL118), ANSES (a French environmental and health agency) recommendations, and health regulations of Besançon University Hospital were applied to determine the rates of non‐conforming dwellings, which were respectively 55.2%, 5.2%, and 19%. Environmental microbiological results and medical data were compared. The whole number of colonies per cubic meter of air was correlated with asthma (P < 0.001) and rhinitis (P = 0.002). Sixty‐seven genera and species were detected in bedrooms. Asthma was correlated to Aspergillus versicolor (P = 0.004) and Cladosporium spp. (P = 0.02). Thresholds of 300 cfu/m³ for A. versicolor or 495 cfu/m³ for Cladosporium spp. are able to discriminate 90% of the asthmatic dwellings. We propose a new protocol to obtain an optimal cost for indoor fungi surveys, excluding surface analyses, and a new guideline to interpret the results based on >1000 cfu/m³ of whole colonies and/or above threshold levels for A. versicolor or Cladosporium spp.     

Separation and quantification of olefins and diolefins in cracked petroleum fractions using silver-ion high performance liquid chromatography

A method using normal phase high performance liquid chromatography for the analysis of olefins and diolefins in cracked petroleum samples is proposed and validated in this work. A silver-impregnated silica column is used to separate olefin and diolefin species, resulting in accurate quantification of both group-types. Separations are completed using an isocratic hexane mobile phase, with no sample clean up or backflushing required. Results from the analysis of 11 model compounds are shown, as well as quantification data for four representative samples; figures of merit are also described.     

The structure solution of the F-type i-ZnMgHf from the X-ray diffraction

ABSTRACT

We report a successful growth of the ZnMgHf F-type icosahedral quasicrystal in the form of faceted single grains. By varying the heat treatment parameters we were able to obtain single crystals with a quality suitable for a single crystal X-ray diffraction which was conducted in a synchrotron facility. Ab initio structure solution by a charge flipping algorithm manifests the existence of two types of Bergman clusters. Clusters are differentiated by the preferential occupation of the high-symmetry positions by hafnium in a rhombic triacontahedron, being the outer shell of the atomic cluster.

This paper is part of a Thematic Issue on The Crystallographic Aspects of Metallic Alloys.     

Good edit similarity learning by loss minimization

Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. However, these methods offer no theoretical guarantee as to the generalization ability and discriminative power of the learned similarities. In this paper, we propose an approach to edit similarity learning based on loss minimization, called GESL. It is driven by the notion of (?,??,??)-goodness, a theory that bridges the gap between the properties of a similarity function and its performance in classification. Using the notion of uniform stability, we derive generalization guarantees that hold for a large class of loss functions. We also provide experimental results on two real-world datasets which show that edit similarities learned with GESL induce more accurate and sparser classifiers than other (standard or learned) edit similarities.