Automation of film densitometry for application in personal monitoring

In this research work, a semi-automatic densitometry system has been developed for large-scale monitoring services by use of film badge dosemeters. The system consists of a charge-coupled device (CCD)-based scanner that can scan optical densities (ODs) up to 4.2, a computer vision algorithm to improve the quality of digitised films and an analyser program to calculate the necessary information, e.g. the mean OD of region of interest and radiation doses. For calibration of the system, two reference films were used. The Microtek scanner International Color Consortium (ICC) profiler is applied for determining the colour attributes of the scanner accurately and a reference of the density step tablet, Bundesanstalt für Materialforschung und-prüfung (BAM) is used for calibrating the automatic conversion of gray-level values to OD values in the range of 0.2-4.0 OD. The system contributes to achieve more objectives and reliable results. So by applying this system, we can digitise a set of 20 films at once and calculate their relative doses less than about 4 min, and meanwhile it causes to avoid disadvantages of manual process and to enhance the accuracy of dosimetry.     

Nanoclay dispersion in a miscible blend: an assessment through rheological analysis

In this study, miscible polymer blend nanocomposite of Poly(ethylene oxide)/Poly(methyl methacrylate), (PEO/PMMA), with sodium montmorillonite (Na⁺-MMT) clay were prepared at a constant concentration of nanoparticles via different solution intercalation methods. The resultant nanocomposites possess different structure and dispersion of Na⁺-MMT clays which are assessed through a combination of transmission electron microscopy (TEM) and X-ray diffraction (XRD). The rheology of the neat blend and two different layered silicate nanocomposites were investigated using linear viscoelastic measurements with a parallel plate rheometry at small strain amplitudes. It was found that regardless of the extent of dispersion, the storage and loss modulus increased by incorporating the nanoparticles into the matrix of PEO/PMMA. Moreover, at low frequencies the rheological response of the nanocomposite in which layered silicates benefit from a better dispersion becomes relatively invariant with frequency and represents a mediocre solid-like behavior in comparison to the nanocomposite in which the nanoparticles are intercalated or agglomerated.     

Numerical and experimental investigations of the response of aluminum cylinders with a cutout subject to axial compression

Understanding how a cutout influences the load bearing capacity and buckling behavior of a cylindrical shell is critical in the design of structural components used in automobiles, aircrafts, and marine applications. Numerical simulation and analysis of moderately thick and thin unstiffened aluminum cylindrical shells (D/t=45, 450 and L/D=2, 5, 10), having a square cutout, subjected to axial compression were systematically carried out in this paper. The investigation examined the influence of the cutout size, cutout location, and the shell aspect ratio (L/D) on the prebuckling, buckling, and postbuckling responses of the cylindrical shells.An experimental investigation on the moderately thick-walled shells was also carried out. A good correlation was observed between the results obtained from the finite element simulation and the experiments. Furthermore, empirical equations, in the form of a ‘buckling load reduction factor’ were developed using the least square regression method. These simple equations could be used to predict the buckling capacities of several specific types of cylindrical shells with a cutout.     

Compatibilization and properties of SAN/EPDM blends with the addition of coagents

SAN and EPDM are not miscible. In this work, the dry blending of SAN and EPDM using Centrex (acrylonitrile/EPDM/styrene graft copolymer) and EPMMA (EPDM‐g‐Mah) as coagents was studied. Centrex content was used at 6–20 wt %. EPMMA content in the mixture was 20 wt %. The effects of coagent type and content on the mechanical properties and morphology were investigated. SEM micrographs of SAN/EPDM/Centrex and SAN/EPDM/EPMMA blends showed that both Centrex and EPMMA have an effective role in forming a finer morphology. For the ternary blends, the addition of coagent resulted in a significant reduction in the size of the dispersed phase. The mechanical properties of SAN/EPDM/coagent blends were improved significantly in comparison to the simple SAN/EPDM blends. SAN/EPDM/Centrex blends showed higher stress‐at‐break and SAN/EPDM/EPMMA blends showed higher impact strength. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Simulation of a spray scrubber performance with Eulerian/Lagrangian approach in the aerosol removing process

In this study, a mathematical model has been developed to simulate the performance of a spray scrubber in an industrial ammonium nitrate plant. The model is based on the Lagrangian approach for the droplets movement and particle source in cell (PSI-CELL) model for calculating the droplet concentration distribution. Consequently, unlike former research, the emphasis is on the droplet dynamic behavior. In the current study, for approaching a realistic model, a droplet size distribution rather than average diameter, and also liquid film formation rather than uniform and constant droplet flow rate has been applied. Also, the Eulerian method has been used for the calculation of the particles removal efficiency and energy balance has been applied on the gas to estimate the droplet size distribution. In the experimental section, the concentration of particles and their size distribution in both inlet and outlet gas of the studied scrubber has been measured for the validation of the predicted particles collection efficiency. In addition, the temperature of the gas at inlet, outlet and in the middle of the tower has been measured for the confirmation of the predicted droplet size distribution in the tower. A good consistency between the model and data has been observed. After the model is validated, it is used to investigate the various variable profiles such as liquid film, total projected surface area of the droplets, velocity profile of the droplets and some of the other parameters in the spray scrubbers.     

Photonics and lasing in liquid crystal materials

Owing to fundamental reasons of symmetry, liquid crystals are soft materials. This softness allows long length-scales, large susceptibilities and the existence of modulated phases, which respond readily to external fields. Liquid crystals with such phases are tunable, self-assembled, photonic band gap materials; they offer exciting opportunities both in basic science and in technology. Since the density of photon states is suppressed in the stop band and is enhanced at the band edges, these materials may be used as switchable filters or as mirrorless lasers. Disordered periodic liquid crystal structures can show random lasing. We highlight recent advances in this rapidly growing area, and discuss future prospects in emerging liquid crystal materials. Liquid crystal elastomers and orientationally ordered nanoparticle assemblies are of particular interest.     

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.     

Microstructural Features Controlling Mechanical Properties in Nb-Mo Microalloyed Steels. Part I: Yield Strength

Low carbon Nb-Mo microalloyed steels show interesting synergies between the “micro”-alloying elements when high strength–high toughness properties are required. Strain accumulation in austenite is enhanced, and therefore grain sizes are refined in the final microstructures. The presence of Mo facilitates the presence of non-polygonal phases, and this constituent modification induces an increment in strength through a substructure formation as well as through an increase in the dislocation density. Regarding fine precipitation and its strengthening effect, the mean size of NbC is reduced in the presence of Mo and their fraction increased, thus enhancing their contribution to yield strength. In this paper, a detailed characterization of the microstructural features of a series of microalloyed steels is described using the electron-backscattered diffraction technique. Mean crystallographic unit sizes, a grain boundary misorientation analysis, and dislocation density measurements are performed. Transmission electron microscopy is carried out to analyze the chemical composition of the precipitates and to estimate their volume fraction. In this first part, the contribution of different strengthening mechanisms to yield strength is evaluated and the calculated value is compared to tensile test results for different coiling temperatures and compositions.     

Effect of Composition and Deformation on Coarse-Grained Austenite Transformation in Nb-Mo Microalloyed Steels

Thermomechanical processing of microalloyed steels containing niobium can be performed to obtain deformed austenite prior to transformation. Accelerated cooling can be employed to refine the final microstructure and, consequently, to improve both strength and toughness. This general rule is fulfilled if the transformation occurs on a quite homogeneous austenite microstructure. Nevertheless, the presence of coarse austenite grains before transformation in different industrial processes is a usual source of concern, and regarding toughness, the coarsest high-angle boundary units would determine its final value. Sets of deformation dilatometry tests were carried out using three 0.06 pct Nb microalloyed steels to evaluate the effect of Mo alloying additions (0, 0.16, and 0.31 pct Mo) on final transformation from both recrystallized and unrecrystallized coarse-grained austenite. Continuous cooling transformation (CCT) diagrams were created, and detailed microstructural characterization was achieved through the use of optical microscopy (OM), field emission gun scanning electron microscopy (FEGSEM), and electron backscattered diffraction (EBSD). The resultant microstructures ranged from polygonal ferrite (PF) and pearlite (P) at slow cooling ranges to bainitic ferrite (BF) accompanied by martensite (M) for fast cooling rates. Plastic deformation of the parent austenite accelerated both ferrite and bainite transformation, moving the CCT curves to higher temperatures and shorter times. However, an increase in the final heterogeneity was observed when BF packets were formed, creating coarse high-angle grain boundary units.     

Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT

A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.