Biodegradable composites with improved barrier properties and transparency from the impregnation of PLA to bacterial cellulose membranes

Poly(lactic acid) (PLA) was impregnated in bacterial cellulose (BC) membranes. BC/PLA films were prepared by solvent casting and mechanical, optical and barrier properties, and biodegradation process were investigated. The transparency of processed films was higher than that of neat BC and increased with PLA content. Moreover, the incorporation of PLA to BC enhanced significantly the water vapor barrier properties of the BC membranes. The bionanocomposites contained a high percentage of cellulose due to the impregnation method that leads to the film with a BC content of 94%, which practically maintains the excellent mechanical properties of BC. However, when increasing the PLA content in the bionanocomposites the mechanical properties decreased slightly with respect to BC. Biodegradation under real soil conditions was determined indirectly through the study of the visual degradation and disintegration, demonstrating that the bionanocomposites were degraded faster than the neat PLA. The successful production of BC/PLA bionanocomposites suggested the possible application of them for active food packaging. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43669.     

Formic acid decomposition on Au catalysts: DFT,microkinetic modeling,and reaction kinetics experiments

A combined theoretical and experimental approach is presented that uses a comprehensive mean‐field microkinetic model, reaction kinetics experiments, and scanning transmission electron microscopy imaging to unravel the reaction mechanism and provide insights into the nature of active sites for formic acid (HCOOH) decomposition on Au/SiC catalysts. All input parameters for the microkinetic model are derived from periodic, self‐consistent, generalized gradient approximation (GGA‐PW91) density functional theory calculations on the Au(111), Au(100), and Au(211) surfaces and are subsequently adjusted to describe the experimental HCOOH decomposition rate and selectivity data. It is shown that the HCOOH decomposition follows the formate (HCOO) mediated path, with 100% selectivity toward the dehydrogenation products (CO₂ + H₂) under all reaction conditions. An analysis of the kinetic parameters suggests that an Au surface in which the coordination number of surface Au atoms is ≤4 may provide a better model for the active site of HCOOH decomposition on these specific supported Au catalysts. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1303–1319, 2014     

Sensitivity and specificity based multiobjective approach for feature selection: Application to cancer diagnosis

The study of the sensitivity and the specificity of a classification test constitute a powerful kind of analysis since it provides specialists with very detailed information useful for cancer diagnosis. In this work, we propose the use of a multiobjective genetic algorithm for gene selection of Microarray datasets. This algorithm performs gene selection from the point of view of the sensitivity and the specificity, both used as quality indicators of the classification test applied to the previously selected genes. In this algorithm, the classification task is accomplished by Support Vector Machines; in addition a 10-Fold Cross-Validation is applied to the resulting subsets. The emerging behavior of all these techniques used together is noticeable, since this approach is able to offer, in an original and easy way, a wide range of accurate solutions to professionals in this area. The effectiveness of this approach is proved on public cancer datasets by working out new and promising results. A comparative analysis of our approach using two and three objectives, and with other existing algorithms, suggest that our proposal is highly appropriate for solving this problem.     

Best practices in measuring algorithm performance for dynamic optimization problems

Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most of works in DOPs report only the best-of-generation fitness, due to its simplicity of computation. Although this measure indicates the best algorithm in terms of fitness, it does not provide any details about the actual strength and weakness of each algorithm. In this article, we conduct a comparative study among algorithms of different search modes via several performance measures to demonstrate their relative advantages. We discuss the role of using different performance measures in drawing balanced conclusions about algorithms for DOPs.     

Parallelism and evolutionary algorithms

This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA     

Materials science and metallurgy of the Caribbean steel drum Part I Fabrication, deformation phenomena and acoustic fundamentals

Steel-drum fabrication, especially the sinking of the drum head (also referred to as the pan) by hand with a hammer, has been examined in detail utilizing light metallography (LM) and transmission electron microscopy (TEM) to characterize residual microstructures corresponding to reductions in thickness of up to 50 % at the bottom of the drum head. Dislocation densities in the low-carbon (0.01–0.05 wt % C), ferritic steels can exceed 10¹⁰ cm^-2. Simulations of simple, ideal, free circular notes utilizing 316 stainless-steel plates (0.05 wt % C), cold rolled to reductions up to 40%, revealed that deformation (per cent cold reduction) has an important effect on the acoustic spectrum, especially harmonic spectra. Harmonic-node splitting was observed for thin circular plates (0.076 cm thick); the frequency difference was 60 Hz at 20% cold reduction and 160 Hz at 40% cold reduction. These dispersion effects, due to deformation-induced microstructures, as well as irregularities in the note geometries and thicknesses, point to the complex and non-linear acoustic features that contribute to the unique sounds of the Caribbean steel drum.     

Analysis of octyl- and nonylphenol and their ethoxylates in water and sediments by liquid chromatography/tandem mass spectrometry

The ubiquitous presence of alkylphenol ethoxylates in the environment as well as concern for endocrine disruption effects in biota caused by their degradation products (such as octyl- and nonylphenol) has raised interest in the environmental fate of these compounds. As part of an effort to model their behavior in a subestuary of the Chesapeake Bay, a quantitative method for the analysis of octyl- and nonylphenol, and their ethoxylates (1-5) in water and sediment was developed. Extraction procedures are based on solid-phase extraction techniques. Identification and quantitation of the analytes is done by liquid chromatography coupled to tandem mass spectrometry. Instrument detection limits for the compounds ranged from 0.1 to 9 pg injected on column, which allowed method detection limits of 0.04-3 ng/L in water and 0.2-13 ng/g of dry weight in sediment. The method was used to analyze water and sediment from the Back River, MD, where concentrations for the individual compounds ranged from <8 to 200 ng/L in water and <9 to 6700 ng/g of dry weight in sediment. Additionally, structural information obtained in the mass spectrometer is presented that supports previous observations that nonylphenol and its ethoxylates are composed mainly of isomers with a tertiary alpha-carbon.     

Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.     

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.