Abstract: Manganese steel (Hadfield) is one of the important
alloys in industry due to its special properties. High work hardening
ability with appropriate toughness and ductility are the properties that
caused this alloy to be used in wear resistance parts and in high
strength condition. Heat treatment is the main process through which
the desired mechanical properties and microstructures are obtained in
Hadfield steel. In this study various heat treatment cycles, differing in
austenising temperature, time and quenching solution are applied. For
this purpose, the same samples of manganese steel was heat treated in
9 different cycles, and then the mechanical properties and
microstructures were investigated. Based on the results of the study,
the optimum heat treatment cycle was obtained.
Abstract: The Brazilian legislation has only established
diagnostic reference levels (DRLs) in terms of Multiple Scan
Average Dose (MSAD) as a quality control parameter for computed
tomography (CT) scanners. Compliance with DRLs can be verified
by measuring the Computed Tomography Kerma Index (Ca,100) with
a pencil ionization chamber or by obtaining the kerma distribution in
CT scans with radiochromic films or rod shape lithium fluoride
termoluminescent dosimeters (TLD-100). TL dosimeters were used
to record kerma profiles and to determine MSAD values of a Bright
Speed model GE CT scanner. Measurements were done with
radiochromic films and TL dosimeters distributed in cylinders
positioned in the center and in four peripheral bores of a standard
polymethylmethacrylate (PMMA) body CT dosimetry phantom.
Irradiations were done using a protocol for adult chest. The
maximum values were found at the midpoint of the longitudinal axis.
The MSAD values obtained with three dosimetric techniques were
compared.
Abstract: Since the pioneering work of Zadeh, fuzzy set theory has been applied to a myriad of areas. Song and Chissom introduced the concept of fuzzy time series and applied some methods to the enrollments of the University of Alabama. In recent years, a number of techniques have been proposed for forecasting based on fuzzy set theory methods. These methods have either used enrollment numbers or differences of enrollments as the universe of discourse. We propose using the year to year percentage change as the universe of discourse. In this communication, the approach of Jilani, Burney, and Ardil is modified by using the year to year percentage change as the universe of discourse. We use enrollment figures for the University of Alabama to illustrate our proposed method. The proposed method results in better forecasting accuracy than existing models.
Abstract: There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA-s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in [17]. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems.
Abstract: The information revealed by derivatives can help to
better characterize digital near-end crosstalk signatures with the
ultimate goal of identifying the specific aggressor signal.
Unfortunately, derivatives tend to be very sensitive to even low
levels of noise. In this work we approximated the derivatives of both
quiet and noisy digital signals using a wavelet-based technique. The
results are presented for Gaussian digital edges, IBIS Model digital
edges, and digital edges in oscilloscope data captured from an actual
printed circuit board. Tradeoffs between accuracy and noise
immunity are presented. The results show that the wavelet technique
can produce first derivative approximations that are accurate to
within 5% or better, even under noisy conditions. The wavelet
technique can be used to calculate the derivative of a digital signal
edge when conventional methods fail.
Abstract: To study on effect of PEG and NaCl stress on
germination and early seedling stages on two cultivar of corn, two
separated experiment were laid out at physiology laboratory, faculty
of Agriculture, Razi University, Kermanshah, Iran in 2009. This
investigation was performed as factorial experiment under Complete
Randomized Design (CRD) with three replications. Cultivar factor
contains of two varieties (sweet corn SC403 and Flint corn SC704)
and five levels of stress (0, -2, -4, -6 and -8 bar). The principal aim of
current study was to compare the two varieties of maize in relative to
the stress conditions. Results indicated that significant decrease was
observed in percentage of germination, germination rate, length of
radicle and plumule and radicle and plumule dry matter. On the basis
of the results, NaCl as compared with PEG had more effect on
germination and early seedling stage and sweet corn had more
resistant than flint corn in both stress conditions.
Abstract: Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is also useful to view the cluster formation at different levels of abstraction. In this paper, we present a technique to generate concept trees from the GSOM. The formation of tree from different spread factor values of GSOM is also investigated and the quality of the trees analyzed. The results show that concept trees can be generated from GSOM, thus, eliminating the need for re-clustering of the data from scratch to obtain a hierarchical view of the data under study.
Abstract: Scouring around a bridge pier is a complex
phenomenon. More laboratory experiments are required to
understand the scour mechanism. This paper focused on time
development of local scour around piers and piles in semi integral
bridges. Laboratory data collected at Hydraulics Laboratory,
University of Malaya was analyzed for this purpose. Tests were
performed with two different uniform sediment sizes and five ranges
of flow velocities. Fine and coarse sediments were tested in the
flume. Results showed that scour depths for both pier and piles
increased with time up to certain levels and after that they became
almost constant. It had been found that scour depths increased when
discharges increased. Coarser sediment also produced lesser scouring
at the piers and combined piles.
Abstract: This work proposes a recursive weighted ELS
algorithm for system identification by applying numerically robust
orthogonal Householder transformations. The properties of the
proposed algorithm show it obtains acceptable results in a noisy
environment: fast convergence and asymptotically unbiased
estimates. Comparative analysis with others robust methods well
known from literature are also presented.
Abstract: This paper presents a new algorithm for the channel estimation of the OFDM system based on a pilot signal for the new generation of high data rate communication systems. In orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels, channel estimation and tracking is generally carried out by transmitting known pilot symbols in given positions of the frequency-time grid. In this paper, we propose to derive an improved algorithm based on the calculation of the mean and the variance of the adjacent pilot signals for a specific distribution of the pilot signals in the OFDM frequency-time grid then calculating of the entire unknown channel coefficients from the equation of the mean and the variance. Simulation results shows that the performance of the OFDM system increase as the length of the channel increase where the accuracy of the estimated channel will be increased using this low complexity algorithm, also the number of the pilot signal needed to be inserted in the OFDM signal will be reduced which lead to increase in the throughput of the signal over the OFDM system in compared with other type of the distribution such as Comb type and Block type channel estimation.
Abstract: Uncertainties of a serial production line affect on the
production throughput. The uncertainties cannot be prevented in a
real production line. However the uncertain conditions can be
controlled by a robust prediction model. Thus, a hybrid model
including autoregressive integrated moving average (ARIMA) and
multiple polynomial regression, is proposed to model the nonlinear
relationship of production uncertainties with throughput. The
uncertainties under consideration of this study are demand, breaktime,
scrap, and lead-time. The nonlinear relationship of production
uncertainties with throughput are examined in the form of quadratic
and cubic regression models, where the adjusted R-squared for
quadratic and cubic regressions was 98.3% and 98.2%. We optimized
the multiple quadratic regression (MQR) by considering the time
series trend of the uncertainties using ARIMA model. Finally the
hybrid model of ARIMA and MQR is formulated by better adjusted
R-squared, which is 98.9%.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Renewed interest in propeller propulsion on aircraft
configurations combined with higher propeller loads lead to the question how the effects of the propulsion on model support disturbances
should be accounted for. In this paper, the determination of engine power effects on support interference of sting-mounted models is
demonstrated by a measurement on a four-engine turboprop aircraft.
CFD results on a more generic model are presented in order to clarify
the possible mechanism behind engine power effects on support
interference. The engine slipstream induces a local change in angle
of sideslip at the model sting thereby influencing the sting near-field and far-field effects. Whether or not the net result of these changes
in the disturbance pattern leads to a significant engine power effect depends on the configuration of the wind tunnel model and the test
setup.
Abstract: In a liberalized electricity market, it is not surprising
that different customers require different power quality (PQ) levels at
different price. Power quality related to several power disturbances is
described by many parameters, so how to define a comprehensive
hierarchy evaluation system of power quality (PQCHES) has become
a concerned issue. In this paper, based on four electromagnetic
compatibility (EMC) levels, the numerical range of each power
disturbance is divided into five grades (Grade I –Grade V), and the
“barrel principle" of power quality is used for the assessment of
overall PQ performance with only one grade indicator. A case study
based on actual monitored data of PQ shows that the site PQ grade
indicates the electromagnetic environment level and also expresses the
characteristics of loads served by the site.
The shortest plank principle of PQ barrel is an incentive
mechanism, which can combine with the rewards/penalty mechanism
(RPM) of consumed energy “on quality demand", to stimulate utilities
to improve the overall PQ level and also stimulate end-user more
“smart" under the infrastructure of future SmartGrid..
Abstract: In this paper an approaches for increasing the
effectiveness of error detection in computer network channels with
Pulse-Amplitude Modulation (PAM) has been proposed. Proposed
approaches are based on consideration of special feature of errors,
which are appearances in line with PAM. The first approach consists
of CRC modification specifically for line with PAM. The second
approach is base of weighted checksums using. The way for
checksum components coding has been developed. It has been shown
that proposed checksum modification ensure superior digital data
control transformation reliability for channels with PAM in compare
to CRC.
Abstract: Antioxidant activities of ethanolic extracts of Ardisia
japonica Blume., Ageartum conyzoides Linn., and Cocculus hirsutus
Linn Diels. leaves was determined qualitatively and quantitatively in
this research. 1, 1-diphenyl-2-picrylhydrazyl (DPPH) free radical
solution was used to investigate free radical scavenging activity of
these leaves extracts. Ascorbic acid (Vitamin C) was used as the
standard. In the present investigation, it is found that all of these
extracts have remarkable antioxidant activities. The EC50 values of
these ethanolic extracts were 12.72 μg/ml for A. japonica, 15.19
μg/ml for A. conyzoides, 10.68 μg/ml for C. hirsutus respectively.
Among these Myanmar medicinal plants, C. hirsutus showed higher
antioxidant activities as well as free radical scavenging activity than
black tea (Camellia sinensis), the famous antioxidant, and A.
japonica and A. conyzoides showed a rather lower antioxidant
activity than tea extracts. According to results from bioassay with
carrot discs infected with Agrobacterium tumefaciens, all extracts
showed anti-tumor activity after 3 weeks of incubation. No gall was
detected in carrot disks treated with C. hirsutus and A. japonica
extracts in the dose of 100ppm and in carrot discs treated with A.
conyzoides extract in the dose of 1000 ppm. Therefore, the research
clearly indicates that these weedy plants of dry farm land are
exceptionally advantageous for human health.
Abstract: Lignocellulosic materials are new targeted source to
produce second generation biofuels like biobutanol. However, this
process is significantly resisted by the native structure of biomass.
Therefore, pretreatment process is always essential to remove
hemicelluloses and lignin prior to the enzymatic hydrolysis.
The goals of pretreatment are removing hemicelluloses and
lignin, increasing biomass porosity, and increasing the enzyme
accessibility. The main goal of this research is to study the important
variables such as pretreatment temperature and time, which can give
the highest total sugar yield in pretreatment step by using dilute
phosphoric acid. After pretreatment, the highest total sugar yield of
13.61 g/L was obtained under an optimal condition at 140°C for 10
min of pretreatment time by using 1.75% (w/w) H3PO4 and at 15:1
liquid to solid ratio. The total sugar yield of two-stage process
(pretreatment+enzymatic hydrolysis) of 27.38 g/L was obtained.
Abstract: In the previous multi-solid models,¤ò approach is
used for the calculation of fugacity in the liquid phase. For the first
time, in the proposed multi-solid thermodynamic model,γ approach
has been used for calculation of fugacity in the liquid mixture.
Therefore, some activity coefficient models have been studied that
the results show that the predictive Wilson model is more appropriate
than others. The results demonstrate γ approach using the predictive
Wilson model is in more agreement with experimental data than the
previous multi-solid models. Also, by this method, generates a new
approach for presenting stability analysis in phase equilibrium
calculations. Meanwhile, the run time in γ approach is less than the
previous methods used ¤ò approach. The results of the new model
present 0.75 AAD % (Average Absolute Deviation) from the
experimental data which is less than the results error of the previous
multi-solid models obviously.
Abstract: Quantitative precipitation forecast (QPF) from
atmospheric model as input to hydrological model in an integrated
hydro-meteorological flood forecasting system has been operational
in many countries worldwide. High-resolution numerical weather
prediction (NWP) models with grid cell sizes between 2 and 14 km
have great potential in contributing towards reasonably accurate QPF.
In this study the potential of two NWP models to forecast
precipitation for a flood-prone area in a tropical region is examined.
The precipitation forecasts produced from the Fifth Generation Penn
State/NCAR Mesoscale (MM5) and Weather Research and
Forecasting (WRF) models are statistically verified with the observed
rain in Kelantan River Basin, Malaysia. The statistical verification
indicates that the models have performed quite satisfactorily for low
and moderate rainfall but not very satisfactory for heavy rainfall.
Abstract: The purpose of this paper is to highlight the
importance of the concept of competitiveness in the supply chain and
to present a conceptual framework for Supply Chain Competitiveness
(SCC). The framework is based on supply chain activities, which are
inputs, necessary for SCC and the benefits which are the outputs of
SCC. A literature review is conducted on key supply chain
competitiveness issues, its determinants, its various dimensions
followed by exploration for SCC. Based on the insights gained, a
conceptual framework for SCC is presented based on activities for
SCC, SCC environment and outcomes of SCC. The information flow
in the conceptual framework is bi-directional at all levels and the
activities are interrelated in a global competitive environment. The
activities include the activities of suppliers, manufacturers and
distributors, giving more emphasis on manufacturers- activities.
Further, implications of various factors such as economic, politicolegal,
technical, socio-cultural, competition, demographic etc. are
also highlighted. The SCC framework is an attempt to cover the
relatively less explored area of supply chain competitiveness. It is
expected that this work will further motivate researchers,
academicians and practitioners to work in this area and offers
conceptual help in providing a directions for supply chain
competitiveness which leads to improvement in the supply chain and
supply chain performance.