Abstract: Nowadays, a great concern is placed on the harmfulness of ultraviolet radiation (UVR) which attacks human bodies. Nanocarbon materials, such as carbon nanotubes (CNTs), carbon nanofibers (CNFs) and graphene, have been considered promising alternatives to shielding materials because of their excellent electrical conductivities, very high surface areas and low densities. In the present work, carbon nanofibers have been synthesized from solutions of Polyacrylonitrile (PAN)/ N,N-dimethylformamide (DMF) by electrospinning method. The carbon nanofibers have been stabilized by oxidation at 250 °C for 2 h in air and carbonized at 750 °C for 1 h in H2/N2. We present the fabrication and characterization of transparent and ultraviolet (UV) shielding CNF/polymer composites. The content of CNF filler has been varied from 0.2% to 0.6 % by weight. UV Spectroscopy has been performed to study the effect of composition on the transmittance of polymer composites.
Abstract: For many enterprises, the delocalization of a part or the totality of their supply chain to low cost countries is the best way to reduce costs and remain competitive against the growing globalized market. This new tendency is driven by logistics advantages, as well as, financial and tax discount offered by the host countries. The objective of this article is to examine the new financial challenges introduced by the project of base erosion and profits shifting (BEPS), published in 2015, and also their impact on the decision of delocalization. In fact, the strategy adopted by multinational firms for determining the transfer price (TP) of goods and services, as well as the shared amount of revenues and expenses have a major impact upon group profit and may contribute to divergent results. In order to get more profit, a coherent decision of delocalization should be based on an evaluation of all the operational and financial characteristics associated with such movement. Therefore, it is interesting to model these new constraints and integrate them in a more global decision model. The established model will enable to measure how much these financial constraints impact the decision of delocalization and will give new helpful directives for enterprise managers.
Abstract: Friction stir welding is the new-fangled and cutting-edge technique in welding applications; it is widely used in the fields of transportation, aerospace, defense, etc. For thriving significant welding joints and properties of friction stir welded components, it is essential to carry out this advanced process in a prescribed systematic procedure. At this moment, Underwater Friction Stir Welding (UFSW) Process is the field of interest to do research work. In the continuous assessment, the study of UFSW process is to comprehend problems occurred in the past and the structure through which the mechanical properties of the welded joints can be value-added and contributes to conclude results an acceptable and resourceful joint. A meticulous criticism is given on how to modify the experimental setup from NFSW to UFSW. It can discern the influence of tool materials, feeds, spindle angle, load, rotational speeds and mechanical properties. By expending the DEFORM-3D simulation software, the achieved outcomes are validated.
Abstract: In this study, it was aimed to determine the thermophysical properties of two different magnetic nanofluids (NiFe2O4-water and CoFe2O4-water). Magnetic nanoparticles were dispersed into the pure water at different volume fractions from 0 vol.% to 4 vol.%. The measurements were performed in the temperature range of 15 oC-55 oC. In order to get better idea on the temperature dependent thermophysical properties of magnetic nanofluids (MNFs), viscosity and thermal conductivity measurements were made. SEM images of both NiFe2O4 and CoFe2O4 nanoparticles were used in order to confirm the average dimensions. The measurements showed that the thermal conductivity of MNFs increased with an increase in the volume fraction as well as viscosity. Increase in the temperature of both MNFs resulted in an increase in the thermal conductivity and a decrease in the viscosity. Based on the measured data, the correlations for both the viscosity and the thermal conductivity were presented with respect to solid volume ratio and temperature. Effective thermal conductivity of the prepared MNFs was also calculated. The results indicated that water based NiFe2O4 nanofluid had higher thermal conductivity than that of the CoFe2O4. Once the viscosity values of both MNFs were compared, almost no difference was observed.
Abstract: This paper aims to determine Fundamental Natural
Frequency (FNF) of a structural composite floor system known as
Chromite. To achieve this purpose, FNFs of studied panels are
determined by development of Finite Element Models (FEMs) in
ABAQUS program. American Institute of Steel Construction (AISC)
code in Steel Design Guide Series 11 presents a fundamental formula
to calculate FNF of a steel framed floor system. This formula has
been used to verify results of the FEMs. The variability in the FNF of
the studied system under various parameters such as dimensions of
floor, boundary conditions, rigidity of main and secondary beams
around the floor, thickness of concrete slab, height of composite
joists, distance between composite joists, thickness of top and bottom
flanges of the open web steel joists, and adding tie beam
perpendicular on the composite joists, is determined. The results
show that changing in dimensions of the system, its boundary
conditions, rigidity of main beam, and also adding tie beam,
significant changes the FNF of the system up to 452.9%, 50.8%, -
52.2%, %52.6%, respectively. In addition, increasing thickness of
concrete slab increases the FNF of the system up to 10.8%.
Furthermore, the results demonstrate that variation in rigidity of
secondary beam, height of composite joist, and distance between
composite joists, and thickness of top and bottom flanges of open
web steel joists insignificant changes the FNF of the studied system
up to -0.02%, -3%, -6.1%, and 0.96%, respectively. Finally, the
results of this study help designer predict occurrence of resonance,
comfortableness, and design criteria of the studied system.
Abstract: The effect of carbon nanofibers (CNFs) on the
electrical properties of Poly(vinylidene fluoride-hexafluoropropylene)
(P(VdF-HFP)) based gel polymer electrolytes has been investigated
in the present work. The length and diameter ranges of CNFs used in
the present work are 5-50 μm and 200-600 nm respectively. The
nanocomposite gel polymer electrolytes have been synthesized by
solution casting technique with varying CNFs content in terms of
weight percentage. Electrochemical impedance analysis demonstrates
that the reinforcement of carbon nanofibers significantly enhances the
ionic conductivity of the polymer electrolyte. The decrease of
crystallinity of P(VdF-HFP) due the addition of CNFs has been
confirmed by X-ray diffraction (XRD). The interaction of CNFs with
various constituents of nanocomposite gel polymer electrolytes has
been assessed by Fourier Transform Infrared (FTIR) spectroscopy.
Moreover CNFs added gel polymer electrolytes offer superior
thermal stability as compared to that of CNFs free electrolytes as
confirmed by Thermogravimetric analysis (TGA).
Abstract: Voting algorithms are extensively used to make
decisions in fault tolerant systems where each redundant module
gives inconsistent outputs. Popular voting algorithms include
majority voting, weighted voting, and inexact majority voters. Each
of these techniques suffers from scenarios where agreements do not
exist for the given voter inputs. This has been successfully overcome
in literature using fuzzy theory. Our previous work concentrated on a
neuro-fuzzy algorithm where training using the neuro system
substantially improved the prediction result of the voting system.
Weight training of Neural Network is sub-optimal. This study
proposes to optimize the weights of the Neural Network using
Artificial Bee Colony algorithm. Experimental results show the
proposed system improves the decision making of the voting
algorithms.
Abstract: Co metal supported on SiO2 and Al2O3 catalysts with
a metal loading varied from 30 of 70 wt.% were evaluated for
decomposition of methane to COx free hydrogen and carbon
nanomaterials. The catalytic runs were carried out from 550-800oC
under atmospheric pressure using fixed bed vertical flow reactor. The
fresh and spent catalysts were characterized by BET surface area
analyzer, XRD, SEM, TEM and TG analysis. The data showed that
50% Co/Al2O3 catalyst exhibited remarkable higher activity at 800oC
with respect to H2 production compared to rest of the catalysts.
However, the catalytic activity and durability was greatly declined at
higher temperature. The main reason for the catalytic inhibition of Co
containing SiO2 catalysts is the higher reduction temperature of
Co2SiO4. TEM images illustrate that the carbon materials with
various morphologies, carbon nanofibers (CNFs), helical-shaped
CNFs and branched CNFs depending on the catalyst composition and
reaction temperature were obtained.
Abstract: Homemade HPC clusters are widely used in many small labs, because they are easy to build and cost-effective. Even though incremental growth is an advantage of clusters, it results in heterogeneous systems anyhow. Instead of adding new nodes to the cluster, we can extend clusters to include some other Internet servers working independently on the same LAN, so that we can make use of their idle times, especially during the night. However extension across a firewall raises some security problems with NFS. In this paper, we propose a method to solve such a problem using SSH tunneling, and suggest a modified structure of the cluster that implements it.
Abstract: Interlaminar shear strength (ILSS) of fiber reinforced polymer composite is an important property for most of the structural applications. Matrix modification is an effective method used to improve the interlaminar shear strength of composite. In this paper, EPON 862/w epoxy system was modified using Tetraethyl orthosilicate (TEOS) electrospun nanofibers (ENFs) which were produced using electrospinning method. Unmodified and nanofibers modified resins were used to fabricate glass fiber reinforced polymer composite (GFRP) using H-VARTM method. The ILSS of the Glass Fiber Reinforced Polymeric Composites (GFRP) was investigated. The study shows that introduction of TEOS ENFs in the epoxy resin enhanced the ILSS of GFRPby 15% with 0.6% wt. fraction of TEOS ENFs.
Abstract: Nanostructured materials have attracted many
researchers due to their outstanding mechanical and physical
properties. For example, carbon nanotubes (CNTs) or carbon
nanofibres (CNFs) are considered to be attractive reinforcement
materials for light weight and high strength metal matrix composites.
These composites are being projected for use in structural
applications for their high specific strength as well as functional
materials for their exciting thermal and electrical characteristics. The
critical issues of CNT-reinforced MMCs include processing
techniques, nanotube dispersion, interface, strengthening mechanisms
and mechanical properties. One of the major obstacles to the effective
use of carbon nanotubes as reinforcements in metal matrix
composites is their agglomeration and poor distribution/dispersion
within the metallic matrix. In order to tap into the advantages of the
properties of CNTs (or CNFs) in composites, the high dispersion of
CNTs (or CNFs) and strong interfacial bonding are the key issues
which are still challenging. Processing techniques used for synthesis
of the composites have been studied with an objective to achieve
homogeneous distribution of carbon nanotubes in the matrix.
Modified mechanical alloying (ball milling) techniques have emerged
as promising routes for the fabrication of carbon nanotube (CNT)
reinforced metal matrix composites. In order to obtain a
homogeneous product, good control of the milling process, in
particular control of the ball movement, is essential. The control of
the ball motion during the milling leads to a reduction in grinding
energy and a more homogeneous product. Also, the critical inner
diameter of the milling container at a particular rotational speed can
be calculated. In the present work, we use conventional and modified
mechanical alloying to generate a homogenous distribution of 2 wt.
% CNT within Al powders. 99% purity Aluminium powder (Acros,
200mesh) was used along with two different types of multiwall
carbon nanotube (MWCNTs) having different aspect ratios to
produce Al-CNT composites. The composite powders were processed
into bulk material by compaction, and sintering using a cylindrical
compaction and tube furnace. Field Emission Scanning electron
microscopy (FESEM), X-Ray diffraction (XRD), Raman
spectroscopy and Vickers macro hardness tester were used to
evaluate CNT dispersion, powder morphology, CNT damage, phase
analysis, mechanical properties and crystal size determination.
Despite the success of ball milling in dispersing CNTs in Al powder,
it is often accompanied with considerable strain hardening of the Al
powder, which may have implications on the final properties of the
composite. The results show that particle size and morphology vary
with milling time. Also, by using the mixing process and sonication
before mechanical alloying and modified ball mill, dispersion of the
CNTs in Al matrix improves.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.