Abstract: Testing is an activity that is required both in the
development and maintenance of the software development life cycle
in which Integration Testing is an important activity. Integration
testing is based on the specification and functionality of the software
and thus could be called black-box testing technique. The purpose of
integration testing is testing integration between software
components. In function or system testing, the concern is with overall
behavior and whether the software meets its functional specifications
or performance characteristics or how well the software and
hardware work together. This explains the importance and necessity
of IT for which the emphasis is on interactions between modules and
their interfaces. Software errors should be discovered early during
IT to reduce the costs of correction. This paper introduces a new type
of integration error, presenting an overview of Integration Testing
techniques with comparison of each technique and also identifying
which technique detects what type of error.
Abstract: In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the preprocessing the extracted signal in cepstrum domain is registered. After classification of digestive diseases, the system selects random samples based on their features and generates the interest nonstationary, long-term signals via inverse transform in cepstral domain which is presented in digital and sonic form as the output. This structure is updatable or on the other word, by receiving a new signal the corresponding disease classification is updated in the feature domain.
Abstract: A concern that researchers usually face in different
applications of Artificial Neural Network (ANN) is determination of
the size of effective domain in time series. In this paper, trial and
error method was used on groundwater depth time series to determine
the size of effective domain in the series in an observation well in
Union County, New Jersey, U.S. different domains of 20, 40, 60, 80,
100, and 120 preceding day were examined and the 80 days was
considered as effective length of the domain. Data sets in different
domains were fed to a Feed Forward Back Propagation ANN with
one hidden layer and the groundwater depths were forecasted. Root
Mean Square Error (RMSE) and the correlation factor (R2) of
estimated and observed groundwater depths for all domains were
determined. In general, groundwater depth forecast improved, as
evidenced by lower RMSEs and higher R2s, when the domain length
increased from 20 to 120. However, 80 days was selected as the
effective domain because the improvement was less than 1% beyond
that. Forecasted ground water depths utilizing measured daily data
(set #1) and data averaged over the effective domain (set #2) were
compared. It was postulated that more accurate nature of measured
daily data was the reason for a better forecast with lower RMSE
(0.1027 m compared to 0.255 m) in set #1. However, the size of input
data in this set was 80 times the size of input data in set #2; a factor
that may increase the computational effort unpredictably. It was
concluded that 80 daily data may be successfully utilized to lower the
size of input data sets considerably, while maintaining the effective
information in the data set.
Abstract: This study examines perception of environmental
approach in small and medium-sized enterprises (SMEs) – the
process by which firms integrate environmental concern into
business. Based on a review of the literature, the paper synthesizes
focus on environmental issues with the reflection in a case study in
the Czech Republic. Two themes of corporate environmentalism are
discussed – corporate environmental orientation and corporate
stances toward environmental concerns. It provides theoretical
material on greening organizational culture that is helpful in
understanding the response of contemporary business to
environmental problems. We integrate theoretical predictions with
empirical findings confronted with reality. Scales to measure these
themes are tested in a survey of managers in 229 Czech firms. We
used the process of in-depth questioning. The research question was
derived and answered in the context of the corresponding literature
and conducted research. A case study showed us that environmental
approach is variety different (depending on the size of the firm) in
SMEs sector. The results of the empirical mapping demonstrate
Czech company’s approach to environment and define the problem
areas and pinpoint the main limitation in the expansion of
environmental aspects. We contribute to the debate for recognition of
the particular role of environmental issues in business reality.
Abstract: The presence of toxic heavy metals in industrial
effluents is one of the serious threats to the environment. Heavy
metals such as Cadmium, Chromium, Lead, Nickel, Zinc, Mercury,
Copper, Arsenic are found in the effluents of industries such as
foundries, electroplating, petrochemical, battery manufacturing,
tanneries, fertilizer, dying, textiles, metallurgical and metal finishing.
Tremendous increase of industrial copper usage and its presence in
industrial effluents has lead to a growing concern about the fate and
effects of Copper in the environment. Percolation of industrial
effluents through soils leads to contamination of ground water and
soils. The transport of heavy metals and their diffusion into the soils
has therefore, drawn the attention of the researchers.
In this study, an attempt has been made to delineate the
mechanisms of transport and fate of copper in terrestrial
environment. Column studies were conducted using perplex glass
square column of dimension side 15 cm and 1.35 m long. The soil
samples were collected from a natural drain near Mohali (India). The
soil was characterized to be poorly graded sandy loam. The soil was
compacted to the field dry density level of about 1.6 g/cm3. Break
through curves for different depths of the column were plotted. The
results of the column study indicated that the copper has high
tendency to flow in the soils and fewer tendencies to get absorbed on
the soil particles. The t1/2 estimates obtained from the studies can be
used for design copper laden wastewater disposal systems.
Abstract: This paper presents a NDT by infrared thermography with excitation CO2 Laser, wavelength of 10.6 μm. This excitation is the controllable heating beam, confirmed by a preliminary test on a wooden plate 1.2 m x 0.9 m x 1 cm. As the first practice, this method is applied to detecting the defect in CFRP heated by the Laser 300 W during 40 s. Two samples 40 cm x 40 cm x 4.5 cm are prepared, one with defect, another one without defect. The laser beam passes through the lens of a deviation device, and heats the samples placed at a determinate position and area. As a result, the absence of adhesive can be detected. This method displays prominently its application as NDT with the composite materials. This work gives a good perspective to characterize the laser beam, which is very useful for the next detection campaigns.
Abstract: The effects of divers carbon substrates were
investigated for the tabtoxin production of an isolated pathogenic
Pseudomonas syringae pv. tabaci, the causal agent of wildfire of
tobacco and are discussed in relation to the bacterium growth. The
isolated organism was grown in batch culture on Woolley's
medium (28°C, 200 rpm, during 5 days). The growth has been
measured by the optical density (OD) at 620 nm and the tabtoxin
production quantified by Escherichia coli (K-12) bioassay
technique. The growth and the tabtoxin production were both
influenced by the substrates (sugars, amino acids, organic acids)
used, each, as a sole carbon source and as a supplement for the
same amino acids. The most significant quantities of tabtoxin were
obtained in presence of some amino acids used as sole carbon
source and/or as supplement.
Abstract: This paper will discuss about an active power generator scheduling method in order to increase the limit level of steady state systems. Some power generator optimization methods such as Langrange, PLN (Indonesian electricity company) Operation, and the proposed Z-Thevenin-based method will be studied and compared in respect of their steady state aspects. A method proposed in this paper is built upon the thevenin equivalent impedance values between each load respected to each generator. The steady state stability index obtained with the REI DIMO method. This research will review the 500kV-Jawa-Bali interconnection system. The simulation results show that the proposed method has the highest limit level of steady state stability compared to other optimization techniques such as Lagrange, and PLN operation. Thus, the proposed method can be used to create the steady state stability limit of the system especially in the peak load condition.
Abstract: Although backpropagation ANNs generally predict
better than decision trees do for pattern classification problems, they
are often regarded as black boxes, i.e., their predictions cannot be
explained as those of decision trees. In many applications, it is
desirable to extract knowledge from trained ANNs for the users to
gain a better understanding of how the networks solve the problems.
A new rule extraction algorithm, called rule extraction from artificial
neural networks (REANN) is proposed and implemented to extract
symbolic rules from ANNs. A standard three-layer feedforward ANN
is the basis of the algorithm. A four-phase training algorithm is
proposed for backpropagation learning. Explicitness of the extracted
rules is supported by comparing them to the symbolic rules generated
by other methods. Extracted rules are comparable with other methods
in terms of number of rules, average number of conditions for a rule,
and predictive accuracy. Extensive experimental studies on several
benchmarks classification problems, such as breast cancer, iris,
diabetes, and season classification problems, demonstrate the
effectiveness of the proposed approach with good generalization
ability.
Abstract: The feature extraction method(s) used to recognize
hand-printed characters play an important role in ICR applications.
In order to achieve high recognition rate for a recognition system, the
choice of a feature that suits for the given script is certainly an
important task. Even if a new feature required to be designed for a
given script, it is essential to know the recognition ability of the
existing features for that script. Devanagari script is being used in
various Indian languages besides Hindi the mother tongue of majority
of Indians. This research examines a variety of feature extraction
approaches, which have been used in various ICR/OCR applications,
in context to Devanagari hand-printed script. The study is conducted
theoretically and experimentally on more that 10 feature extraction
methods. The various feature extraction methods have been evaluated
on Devanagari hand-printed database comprising more than 25000
characters belonging to 43 alphabets. The recognition ability of the
features have been evaluated using three classifiers i.e. k-NN, MLP
and SVM.
Abstract: As the majority of faults are found in a few of its
modules so there is a need to investigate the modules that are
affected severely as compared to other modules and proper
maintenance need to be done in time especially for the critical
applications. As, Neural networks, which have been already applied
in software engineering applications to build reliability growth
models predict the gross change or reusability metrics. Neural
networks are non-linear sophisticated modeling techniques that are
able to model complex functions. Neural network techniques are
used when exact nature of input and outputs is not known. A key
feature is that they learn the relationship between input and output
through training. In this present work, various Neural Network Based
techniques are explored and comparative analysis is performed for
the prediction of level of need of maintenance by predicting level
severity of faults present in NASA-s public domain defect dataset.
The comparison of different algorithms is made on the basis of Mean
Absolute Error, Root Mean Square Error and Accuracy Values. It is
concluded that Generalized Regression Networks is the best
algorithm for classification of the software components into different
level of severity of impact of the faults. The algorithm can be used to
develop model that can be used for identifying modules that are
heavily affected by the faults.
Abstract: Achievement motivation is believed to promote
giftedness attracting people to invest in many programs to adopt
gifted students providing them with challenging activities.
Intellectual giftedness is founded on the fluid intelligence and
extends to more specific abilities through the growth and inputs from
the achievement motivation. Acknowledging the roles played by the
motivation in the development of giftedness leads to an effective
nurturing of gifted individuals. However, no study has investigated
the direct and indirect effects of the achievement motivation and
fluid intelligence on intellectual giftedness. Thus, this study
investigated the contribution of motivation factors to giftedness
development by conducting tests of fluid intelligence using Cattell
Culture Fair Test (CCFT) and analytical abilities using culture
reduced test items covering problem solving, pattern recognition,
audio-logic, audio-matrices, and artificial language, and self report
questionnaire for the motivational factors. A number of 180 highscoring
students were selected using CCFT from a leading university
in Malaysia. Structural equation modeling was employed using Amos
V.16 to determine the direct and indirect effects of achievement
motivation factors (self confidence, success, perseverance,
competition, autonomy, responsibility, ambition, and locus of
control) on the intellectual giftedness. The findings showed that the
hypothesized model fitted the data, supporting the model postulates
and showed significant and strong direct and indirect effects of the
motivation and fluid intelligence on the intellectual giftedness.
Abstract: Inter-organizational Workflow (IOW) is commonly
used to support the collaboration between heterogeneous and
distributed business processes of different autonomous organizations
in order to achieve a common goal. E-government is considered as an
application field of IOW. The coordination of the different
organizations is the fundamental problem in IOW and remains the
major cause of failure in e-government projects. In this paper, we
introduce a new coordination model for IOW that improves the
collaboration between government administrations and that respects
IOW requirements applied to e-government. For this purpose, we
adopt a Multi-Agent approach, which deals more easily with interorganizational
digital government characteristics: distribution,
heterogeneity and autonomy. Our model integrates also different
technologies to deal with the semantic and technologic
interoperability. Moreover, it conserves the existing systems of
government administrations by offering a distributed coordination
based on interfaces communication. This is especially applied in
developing countries, where administrations are not necessary
equipped with workflow systems. The use of our coordination
techniques allows an easier migration for an e-government solution
and with a lower cost. To illustrate the applicability of the proposed
model, we present a case study of an identity card creation in Tunisia.
Abstract: Due to the high percentage of induction motors in industrial market, there exist a large opportunity for energy savings. Replacement of working induction motors with more efficient ones can be an important resource for energy savings. A calculation of energy savings and payback periods, as a result of such a replacement, based on nameplate motor efficiency or manufacture-s data can lead to large errors [1]. Efficiency of induction motors (IMs) can be extracted using some procedures that use the no-load test results. In the cases that we must estimate the efficiency on-line, some of these procedures can-t be efficient. In some cases the efficiency estimates using the rating values of the motor, but these procedures can have errors due to the different working condition of the motor. In this paper the efficiency of an IM estimated by using the genetic algorithm. The results are compared with the measured values of the torque and power. The results show smaller errors for this procedure compared with the conventional classical procedures, hence the cost of the equipments is reduced and on-line estimation of the efficiency can be made.
Abstract: A new low-voltage floating gate MOSFET (FGMOS)
based squarer using square law characteristic of the FGMOS is
proposed in this paper. The major advantages of the squarer are simplicity,
rail-to-rail input dynamic range, low total harmonic distortion,
and low power consumption. The proposed circuit is biased without
body effect. The circuit is designed and simulated using SPICE in
0.25μm CMOS technology. The squarer is operated at the supply
voltages of ±0.75V . The total harmonic distortion (THD) for the
input signal 0.75Vpp at 25 KHz, and maximum power consumption
were found to be less than 1% and 319μW respectively.
Abstract: Increasing energy absorption is a significant parameter
in vehicle design. Absorbing more energy results in decreasing
occupant damage. Limitation of the deflection in a side impact results
in decreased energy absorption (SEA) and increased peak load (PL).
Hence a high crash force jeopardizes passenger safety and vehicle
integrity. The aims of this paper are to determine suitable dimensions
and material of a square beam subjected to side impact, in order to
maximize SEA and minimize PL. To achieve this novel goal, the
geometric parameters of a square beam are optimized using the
response surface method (RSM).multi-objective optimization is
performed, and the optimum design for different response features is
obtained.
Abstract: The recent global financial problem urges government
to play role in stimulating the economy due to the fact that private
sector has little ability to purchase during the recession. A concerned
question is whether the increased government spending crowds out
private consumption and whether it helps stimulate the economy. If
the government spending policy is effective; the private consumption
is expected to increase and can compensate the recent extra
government expense. In this study, the government spending is
categorized into government consumption spending and government
capital spending. The study firstly examines consumer consumption
along the line with the demand function in microeconomic theory.
Three categories of private consumption are used in the study. Those
are food consumption, non food consumption, and services
consumption. The dynamic Almost Ideal Demand System of the three
categories of the private consumption is estimated using the Vector
Error Correction Mechanism model. The estimated model indicates
the substituting effects (negative impacts) of the government
consumption spending on budget shares of private non food
consumption and of the government capital spending on budget share
of private food consumption, respectively. Nevertheless the result
does not necessarily indicate whether the negative effects of changes
in the budget shares of the non food and the food consumption means
fallen total private consumption. Microeconomic consumer demand
analysis clearly indicates changes in component structure of
aggregate expenditure in the economy as a result of the government
spending policy. The macroeconomic concept of aggregate demand
comprising consumption, investment, government spending (the
government consumption spending and the government capital
spending), export, and import are used to estimate for their
relationship using the Vector Error Correction Mechanism model.
The macroeconomic study found no effect of the government capital
spending on either the private consumption or the growth of GDP
while the government consumption spending has negative effect on
the growth of GDP. Therefore no crowding out effect of the
government spending is found on the private consumption but it is
ineffective and even inefficient expenditure as found reducing growth
of the GDP in the context of Thailand.
Abstract: Large scale systems such as computational Grid is
a distributed computing infrastructure that can provide globally
available network resources. The evolution of information processing
systems in Data Grid is characterized by a strong decentralization of
data in several fields whose objective is to ensure the availability and
the reliability of the data in the reason to provide a fault tolerance
and scalability, which cannot be possible only with the use of the
techniques of replication. Unfortunately the use of these techniques
has a height cost, because it is necessary to maintain consistency
between the distributed data. Nevertheless, to agree to live with
certain imperfections can improve the performance of the system by
improving competition. In this paper, we propose a multi-layer protocol
combining the pessimistic and optimistic approaches conceived
for the data consistency maintenance in large scale systems. Our
approach is based on a hierarchical representation model with tree
layers, whose objective is with double vocation, because it initially
makes it possible to reduce response times compared to completely
pessimistic approach and it the second time to improve the quality
of service compared to an optimistic approach.
Abstract: Power Spectral Density (PSD) of quasi-stationary processes can be efficiently estimated using the short time Fourier series (STFT). In this paper, an algorithm has been proposed that computes the PSD of quasi-stationary process efficiently using offline autoregressive model order estimation algorithm, recursive parameter estimation technique and modified sliding window discrete Fourier Transform algorithm. The main difference in this algorithm and STFT is that the sliding window (SW) and window for spectral estimation (WSA) are separately defined. WSA is updated and its PSD is computed only when change in statistics is detected in the SW. The computational complexity of the proposed algorithm is found to be lesser than that for standard STFT technique.
Abstract: This study investigated a strategy of blending lead-laden sludge and Al-rich precursors to reduce the release of metals from the stabilized products. Using PbO as the simulated lead-laden sludge to sinter with γ-Al2O3 by Pb:Al molar ratios of 1:2 and 1:12, PbAl2O4 and PbAl12O19 were formed as final products during the sintering process, respectively. By firing the PbO + γ-Al2O3 mixtures with different Pb/Al molar ratios at 600 to 1000 °C, the lead transformation was determined through X-ray diffraction (XRD) data. In Pb/Al molar ratio of 1/2 system, the formation of PbAl2O4 is initiated at 700 °C, but an effective formation was observed above 750 °C. An intermediate phase, Pb9Al8O21, was detected in the temperature range of 800-900 °C. However, different incorporation behavior for sintering PbO with Al-rich precursors at a Pb/Al molar ratio of 1/12 was observed during the formation of PbAl12O19 in this system. In the sintering process, both temperature and time effect on the formation of PbAl2O4 and PbAl12O19 phases were estimated. Finally, a prolonged leaching test modified from the U.S. Environmental Protection Agency-s toxicity characteristic leaching procedure (TCLP) was used to evaluate the durability of PbO, Pb9Al8O21, PbAl2O4 and PbAl12O19 phases. Comparison for the leaching results of the four phases demonstrated the higher intrinsic resistance of PbAl12O19 against acid attack.