Study of the effective parameters on mechanical and electrical properties of carbon black filled PP/PA6 microfibrillar composites

In this study the electrical and mechanical properties of microfibrillar polypropylene (PP)/polyamide6 (PA6) blend filled with super conductive carbon black (CB) have been investigated. In situ microfibrillar PP/PA6 composites filled with CB are produced using a single screw extruder equipped with a spinneret. Glycidyl methacrylate (GMA) grafted polypropylene (PP-g-GMA) is used as the compatibilizer. To investigate the effects of extensional flow on the microstructure, electrical and mechanical properties, three adaptors with various convergence angles were designed, prepared and applied between the extruder and the spinneret. To optimize the effects of processing and material parameters on the electrical and mechanical properties, the Taguchi method of experimental design is used. Material and processing factors which are studied include: concentration of PA6, compatibilizer level, CB concentration, drawing speed of melt spinning line, adaptor angle, order of mixing and temperature profile along the extruder. The results show an increase in DC conductivity of up to 10¹¹ times in comparison with pure PA6, by increasing the concentration of CB, drawing speed, adaptor angle and optimizing other parameters. By optimizing processing and material factors studied here, strength of microfibrillar structured composites is increased of up to 80% in comparison to pure PP.     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...     

Practical scalable image analysis and indexing using Hadoop

The ability to handle very large amounts of image data is important for image analysis, indexing and retrieval applications. Sadly, in the literature, scalability aspects are often ignored or glanced over, especially with respect to the intricacies of actual implementation details. In this paper we present a case-study showing how a standard bag-of-visual-words image indexing pipeline can be scaled across a distributed cluster of machines. In order to achieve scalability, we investigate the optimal combination of hybridisations of the MapReduce distributed computational framework which allows the components of the analysis and indexing pipeline to be effectively mapped and run on modern server hardware. We then demonstrate the scalability of the approach practically with a set of image analysis and indexing tools built on top of the Apache Hadoop MapReduce framework. The tools used for our experiments are freely available as open-source software, and the paper fully describes the nuances of their implementation.     

Study on the performance of Cd2+ sorption using dimethylethylenediamine-modified zinc-based MOF (ZIF-8-mmen): optimization of the process by RSM technique

ABSTRACT

Cadmium as a highly toxic metal is released into the environment through paper production, metal processing, phosphate fertilizers, insecticides, and treatment of wastewater. Cadmium also inhibits the body activities and is very toxic for kidney and other organisms. In the current study, zinc-based metal–organic framework, zeolitic imidazolate framework (ZIF)-8, was synthesized and modified by dimethylethylenediamine (ZIF-8-mmen) for the removal of cadmium. To optimize the experiments, response surface methodology was applied with three variables including pH, adsorbent dosage, and contact time using central composite design. The optimum conditions for pH, dosage, and time were 2, 0.1 g, and 89 min, respectively, with removal efficiency of 85.38%. The Langmuir isotherm (q _m = 1000 mg/g) indicates the monolayer adsorption. The kinetic studies reveal that the Lagergren model was predominant and cadmium was not chemisorbed. Thermodynamic parameters show spontaneous, endothermic, and physisorption processes.     

Transforming Vulnerability Indexing for Saltwater Intrusion into Risk Indexing through a Fuzzy Catastrophe Scheme

Mapping vulnerability to Saltwater Intrusion (SWI) in coastal aquifers is studied in this paper using the GALDIT framework but with a novelty of transforming the concept of vulnerability indexing to risk indexing. GALDIT is the acronym of 6 data layers, which are put consensually together to invoke a sense of vulnerability to the intrusion of saltwater against aquifers with freshwater. It is a scoring system of prescribed rates to account for local variations; and prescribed weights to account for relative importance of each data layer but these suffer from subjectivity. Another novelty of the paper is to use fuzzy logic to learn rate values and catastrophe theory to learn weight values and these together are implemented as a scheme and hence Fuzzy-Catastrophe Scheme (FCS). The GALDIT data layers are divided into two groups of Passive Vulnerability Indices (PVI) and Active Vulnerability Indices (AVI), where their sum is Total Vulnerability Index (TVI) and equivalent to GALDIT. Two additional data layers (Pumping and Water table decline) are also introduced to serve as Risk Actuation Index (RAI). The product of TVI and RAI yields Risk Indices. The paper applies these new concepts to a study area, subject to groundwater decline and a possible saltwater intrusion problem. The results provide a proof-of-concept for PVI, AVI, RAI and RI by studying their correlation with groundwater quality samples using the fraction of saltwater (f_sea), Groundwater Quality Indices (GQI) and Piper diagram. Significant correlations between the appropriate values are found and these provide a new insight for the study area.

     

A model to predict the solubility and permeability of gaseous penetrant in the glassy polymeric membrane at high pressure

In this work, the transport properties of gaseous penetrant through several dense glassy polymeric membranes are studied. The nonequilibrium lattice fluid (NELF) in conjunction with the modified Fick's law and dual mode sorption model was used to simulate the gas transport in glassy polymeric membranes. The approach is based on the sorption, diffusion, in which solubility is calculated based on the NELF model, and diffusion coefficient is obtained from the product thermodynamic coefficient and molecular mobility. The governing equation is solved by the finite element method using COMSOL multi-physics software. The developed model for gas permeability of glassy polymeric membrane can be applied in a wide range of pressure and temperature. The comparison of the calculated permeability and solubility of gasses with the experimental data represented the ability of the developed model. Increasing feed gas temperature increases the gas permeability, while this variation leads to lower gas solubility in the glassy polymeric membranes. The effect of feed temperature and pressure on permeability and solubility is investigated, and the experimental data from literature are described by the developed model. A good prediction of the experimental data can be observed over the considered condition.     

A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization

In this paper, metamodeling and five well-known metaheuristic optimization algorithms were used to reduce the weight and improve crash and NVH attributes of a vehicle simultaneously. A high-fidelity full vehicle model is used to analyze peak acceleration, intrusion and component’s internal-energy under Full-Frontal, Offset-Frontal, and Side crash scenarios as well as vehicle natural frequencies. The radial basis functions method is used to approximate the structural responses. A nonlinear surrogate-based mass minimization was formulated and solved by five different optimization algorithms under crash-vibration constraints. The performance of these algorithms is investigated and discussed.     

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.      

Preparative scale synthesis ofO-glycosides and of a disaccharide by electrochemical oxidation of phenylS-glycosides

Summary O-glycosides were synthesized by electrochemical oxidation of phenylS-glycosides in the presence of primary alcohols in acetonitrile. Similarly, a -linked disaccharide was obtained selectively by oxidation of phenylS-glycoside in the presence of a sugar alcohol. Electrosyntheses were performed under controlled potential or at constant current, in an undivided cell, on a large scale. 1 to 60 g of phenylS-glycosides in 0.5 to 1 dm³ of acetonitrile were converted with chemical yields in the range of 65–75%.     

Additive Manufacturing of Hard Magnetic Passive Shims to Increase Field Homogeneity of a Halbach Magnet

Obtaining a highly homogeneous magnetic field is desired for field-controlled applications. For example, the resolution of magnetic analysis methods can be improved by generating a stronger and more homogeneous field over the region of interest (ROI). A set of 3D-printed passive shims is fabricated using additive manufacturing to improve the magnetic field homogeneity of a Halbach magnet assembly. The feedstock is a custom acrylonitrile butadiene styrene (ABS)-hard magnet composite filament filled with 60% wt. isotropic NdFeB. Additionally, a method for investigating the remanence is developed and validated. The result reveals a good agreement between the new method and existing measurement techniques for the remanence of permanent magnets. It is also shown that the additive manufacturing procedure has negligible effects on the magnetic properties. Performing a parametric study over a rectangular ROI, an optimized shim configuration is achieved. In the optimized and 3D-printed configuration, the average norm of the magnetic flux density, B_norm, is increased by 13% and, more importantly, a 43% increase in the magnetic uniformity is obtained. These results highlight the great potential of freeform manufacturing, namely, additive manufacturing, to tailor the properties of magnet structures.