Kinetic multi-layer model of gas-particle interactions in aerosols and clouds (KM-GAP): linking condensation, evaporation and chemical reactions of organics, oxidants and water

We present a novel kinetic multi-layer model for gas-particle interactions in aerosols and clouds (KMGAP) that treats explicitly all steps of mass transport and chemical reaction of semi-volatile species partitioning between gas phase, particle surface and particle bulk. KMGAP is based on the PRA model framework (P¨oschl-Rudich- Ammann, 2007), and it includes gas phase diffusion, reversible adsorption, surface reactions, bulk diffusion and reaction, as well as condensation, evaporation and heat transfer. The size change of atmospheric particles and the temporal evolution and spatial profile of the concentration of individual chemical species can be modeled along with gas uptake and accommodation coefficients. Depending on the complexity of the investigated system and the computational constraints, unlimited numbers of semi-volatile species, chemical reactions, and physical processes can be treated, and the model shall help to bridge gaps in the understanding and quantification of multiphase chemistry and microphysics in atmospheric aerosols and clouds. In this study we demonstrate how KM-GAP can be used to analyze, interpret and design experimental investigations of changes in particle size and chemical composition in response to condensation, evaporation, and chemical reaction. For the condensational growth of water droplets, our kinetic model results provide a direct link between laboratory observations and molecular dynamic simulations, confirming that the accommodation coefficient of water at 270K is close to unity (Winkler et al., 2006). Literature data on the evaporation of dioctyl phthalate as a function of particle size and time can be reproduced, and the model results suggest that changes in the experimental conditions like aerosol particle concentration and chamber geometry may influence the evaporation kinetics and can be optimized for efficient probing of specific physical effects and parameters. With regard to oxidative aging of organic aerosol particles, we illustrate how the formation and evaporation of volatile reaction products like nonanal can cause a decrease in the size of oleic acid particles exposed to ozone.

Transport properties of multi-layer fabric based on electrospun nanofiber mats as a breathable barrier textile material

Layered fabric systems with an electrospun nanofiber web layered onto a sandwich of woven fabric were developed toexamine the feasibility of developing breathable barrier textile materials. Some parameters of nanofiber mats, including thetime of electrospinning and the polymer solution concentration, were designed to change and barrier properties ofspecimens were compared. Air permeability, water vapor transmission, and water repellency (Bundesmann and hydrostaticpressure tests) were assessed as indications of comfort and barrier performance of different samples. These performancesof layered nanofiber fabrics were compared with a well-known water repellent breathable multi-layered fabric(Gortex).Multi-layered electrospun nanofiber mats equipped fabric (MENMEF) showed better performance in windproof propertythan Gortex fabric. Also, water vapor permeability of MENMEF was in a range of normal woven sport and work clothing.Comparisons of barrier properties of MENMEF and the currently available PTFE coated materials showed that, thoseproperties could be achieved by layered fabric systems with electrospun nanofiber mats.

Production and applications of multi-layer 3D fabrics

In this paper, 3D textile structures, their types and applications have been discussed. Woven structures especially multi-layer woven structures are discussed in detail. A simple and quick method to produce 3D textile woven preforms on conventional looms is also given at the end of this paper. 3D textile fabrics give composite structures weight reduction with improved mechanical properties in comparison to traditionally used materials.

Multi-layer implantable antenna for closed loop deep brain stimulation system

A multi-layer circular planar inverted-F antenna is designed and simulated at the industrial, scientific, and medical (ISM) band of 915 MHz for closed loop deep brain stimulation implant. The ISM band is considered due to the capabilities of small antenna size, high data rate, and long transmission range. In the proposed four-layer antenna, the top three radiating layers are meandered, and a high permittivity substrate and superstrate materials are used to limit the radius and the height of the antenna to 3.5 mm and 2.2 mm, respectively. The bottom layer works as a ground plate. The Roger RO3210 of εr = 10.2 and δ = 0.003 is used as a dielectric substrate and superstrate. The resonance frequency of the proposed antenna is 915 MHz with a bandwidth of 12 MHz at the return loss of -10 dB in free space. The stacked layered structure reduces the antenna size, and the circular shape makes it easily implantable into the human head. The antenna parameters (e.g. 3D gain pattern), SAR value, and electric field distribution within a six layers spherical head model are evaluated by using the finite element method (FEM). The feasibility of the wireless transmission of power, control and command signal to the implant in the human head is also examined. © 2012 IEEE.

A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services

This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

Effect of Gamma Irradiation on Caprolactam Migration from Multi layer Polyamide 6 Films into Food Simulants: Development and Validation of a Gas Chromatographic Method

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.

Evaluation of a multi variated analysis for the selection of t-tbar multijet events in the current CMS implemented environment at LHC

In the present study we are using multi variate analysis techniques to discriminate signal from background in the fully hadronic decay channel of ttbar events. We give a brief introduction to the role of the Top quark in the standard model and a general description of the CMS Experiment at LHC. We have used the CMS experiment computing and software infrastructure to generate and prepare the data samples used in this analysis. We tested the performance of three different classifiers applied to our data samples and used the selection obtained with the Multi Layer Perceptron classifier to give an estimation of the statistical and systematical uncertainty on the cross section measurement.

High-Speed Laser Processing of Thin Single and Multi-Layer Films

Theoretical models are developed for the continuous-wave and pulsed laser incision and cut of thin single and multi-layer films. A one-dimensional steady-state model establishes the theoretical foundations of the problem by combining a power-balance integral with heat flow in the direction of laser motion. In this approach, classical modelling methods for laser processing are extended by introducing multi-layer optical absorption and thermal properties. The calculation domain is consequently divided in correspondence with the progressive removal of individual layers. A second, time-domain numerical model for the short-pulse laser ablation of metals accounts for changes in optical and thermal properties during a single laser pulse. With sufficient fluence, the target surface is heated towards its critical temperature and homogeneous boiling or "phase explosion" takes place. Improvements are seen over previous works with the more accurate calculation of optical absorption and shielding of the incident beam by the ablation products. A third, general time-domain numerical laser processing model combines ablation depth and energy absorption data from the short-pulse model with two-dimensional heat flow in an arbitrary multi-layer structure. Layer removal is the result of both progressive short-pulse ablation and classical vaporisation due to long-term heating of the sample. At low velocity, pulsed laser exposure of multi-layer films comprising aluminium-plastic and aluminium-paper are found to be characterised by short-pulse ablation of the metallic layer and vaporisation or degradation of the others due to thermal conduction from the former. At high velocity, all layers of the two films are ultimately removed by vaporisation or degradation as the average beam power is increased to achieve a complete cut. The transition velocity between the two characteristic removal types is shown to be a function of the pulse repetition rate. An experimental investigation validates the simulation results and provides new laser processing data for some typical packaging materials.

Microstructure of MmM(5)/Mg multi-layer hydrogen storage films prepared by magnetron sputtering

Multi-layer hydrogen storage thin films with Mg and MmNi(3.5)(CoAlMn)(1.5) (here Mm denotes La-rich mischmetal) as alternative layers were prepared by direct current magnetron sputtering. Transmission electron microscopy investigation shows that the microstructure of the MmNi(3.5)(CoAlMn)(1.5) and Mg layers are significantly different although their deposition conditions are the same. The MmNi(3.5)(CoAlMn)(1.5) layer is composed of two regions: one is an amorphous region approximately 4 nm thick at the bottom of the layer and the other is a nanocrystalline region on top of the amorphous region. The Mg layer is also composed of two regions: one is a randomly orientated nanocrystalline region 50 nm thick at the bottom of the layer and the other is a columnar crystallite region on top of the nanocrystalline region. These Mg columnar crystallites have their [001] directions parallel to the growth direction and the average lateral size of these columnar crystallites is about 100 nm. A growth mechanism of the multi-layer thin films is discussed based on the experiment results. Wiley-Liss, Inc.

Microstructure of MmM(5)/Mg multi-layer films prepared by magnetron sputtering

Microstructure of MmNi(3.5)(CoAlMn)(1.5)/Mg (here Mm denotes La-rich mischmetal) multi-layer hydrogen storage thin films prepared by direct current magnetron sputtering was investigated by cross-sectional transmission electron microscopy (XTEM). It was shown that the MMM5 layers are composed of two regions: an amorphous region with a thickness of similar to 4nm at the bottom of the layers and a randomly orientated nanocrystallite region on the top of the amorphous region and the Mg layers consist of typical columnar crystallite with their [001] direction nearly parallel to the growth direction. The mechanism for the formation of the above microstructure characteristics in the multi-layer thin films has been proposed. Based on the microstructure feature of the multi-layer films, mechanism for the apparent improvement of hydrogen absorption/desorption kinetics was discussed. (c) 2005 Elsevier B.V. All rights reserved.