Abstract: Information and Communication Technologies (ICT) in mathematical education is a very active field of research and innovation, where learning is understood to be meaningful and grasping multiple linked representation rather than rote memorization, a great amount of literature offering a wide range of theories, learning approaches, methodologies and interpretations, are generally stressing the potentialities for teaching and learning using ICT. Despite the utilization of new learning approaches with ICT, students experience difficulties in learning concepts relevant to understanding mathematics, much remains unclear about the relationship between the computer environment, the activities it might support, and the knowledge that might emerge from such activities. Many questions that might arise in this regard: to what extent does the use of ICT help students in the process of understanding and solving tasks or problems? Is it possible to identify what aspects or features of students' mathematical learning can be enhanced by the use of technology? This paper will highlight the interest of the integration of information and communication technologies (ICT) into the teaching and learning of mathematics (quadratic functions), it aims to investigate the effect of four instructional methods on students- mathematical understanding and problem solving. Quantitative and qualitative methods are used to report about 43 students in middle school. Results showed that mathematical thinking and problem solving evolves as students engage with ICT activities and learn cooperatively.
Abstract: It is observed that the Weighted least-square (WLS)
technique, including the modifications, results in equiripple error
curve. The resultant error as a percent of the ideal value is highly
non-uniformly distributed over the range of frequencies for which the
differentiator is designed. The present paper proposes a modification
to the technique so that the optimization procedure results in lower
maximum relative error compared to the ideal values. Simulation
results for first order as well as higher order differentiators are given
to illustrate the excellent performance of the proposed method.
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: 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: Cloud Computing is a new technology that helps us to
use the Cloud for compliance our computation needs. Cloud refers to a scalable network of computers that work together like Internet. An
important element in Cloud Computing is that we shift processing, managing, storing and implementing our data from, locality into the
Cloud; So it helps us to improve the efficiency. Because of it is new
technology, it has both advantages and disadvantages that are
scrutinized in this article. Then some vanguards of this technology
are studied. Afterwards we find out that Cloud Computing will have
important roles in our tomorrow life!
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: In this paper we discuss the problems of the long-term management policy of Lake Peipsi and the roles of natural and anthropogenic factors in the ecological state of the lake. The reduction of the pollution during the last 15 years could not give significant changes of the chemical composition of the water, what implicates the essential role that natural factors have on the ecological state of lake. One of the most important factors having impact on the hydrochemical cycles and ecological state is the hydrological regime which is clearly expressed in L. Peipsi. The absence on clear interrelations of climate cycles and nutrients suggest that complex abiotic and biotic interactions, which take place in the lake ecosystem, plays a significant role in the matter circulation mechanism within lake.
Abstract: The removal of hydrogen sulphide is required for reasons of health, odour problems, safety and corrosivity problems. The means of removing hydrogen sulphide mainly depend on its concentration and kind of medium to be purified. The paper deals with a method of hydrogen sulphide removal from the air by its catalytic oxidation to elemental sulphur with the use of Fe-EDTA complex. The possibility of obtaining fibrous filtering materials able to remove small concentrations of H2S from the air were described. The base of these materials is fibrous ion exchanger with Fe(III)- EDTA complex immobilized on their functional groups. The complex of trivalent iron converts hydrogen sulphide to elemental sulphur. Bivalent iron formed in the reaction is oxidized by the atmospheric oxygen, so complex of trivalent iron is continuously regenerated and the overall process can be accounted as pseudocatalytic. In the present paper properties of several fibrous catalysts based on ion exchangers with different chemical nature (weak acid,weak base and strong base) were described. It was shown that the main parameters affecting the process of catalytic oxidation are:concentration of hydrogen sulphide in the air, relative humidity of the purified air, the process time and the content of Fe-EDTA complex in the fibres. The data presented show that the filtering layers with anion exchange package are much more active in the catalytic processes of hydrogen sulphide removal than cation exchanger and inert materials. In the addition to the nature of the fibres relative air humidity is a critical factor determining efficiency of the material in the air purification from H2S. It was proved that the most promising carrier of the Fe-EDTA catalyst for hydrogen sulphide oxidation are Fiban A-6 and Fiban AK-22 fibres.
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: 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: In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems.
Abstract: Since the late 1980s, the new phenomena of 'employment subcentres' or 'polycentricity' has appeared in the metropolises of North American and Western Europe and it has been an interesting topic for academics and researchers. This paper specifically uses one case study-Guangzhou to explore the development and the mechanism of employment subcentres and polycentricity in Chinese metropolises by spatial analysis method on the basis of the first economic census data. In conclusion, the paper regards that the employment subcentres and polycentricity has existed in Chinese metropolises. And that, the mechanism of them is mainly from the secondary industry instead of the tertiary industry in North American and Western Europe
Abstract: The use of radar in Quantitative Precipitation Estimation (QPE) for radar-rainfall measurement is significantly beneficial. Radar has advantages in terms of high spatial and temporal condition in rainfall measurement and also forecasting. In Malaysia, radar application in QPE is still new and needs to be explored. This paper focuses on the Z/R derivation works of radarrainfall estimation based on rainfall classification. The works developed new Z/R relationships for Klang River Basin in Selangor area for three different general classes of rain events, namely low (10mm/hr, 30mm/hr) and also on more specific rain types during monsoon seasons. Looking at the high potential of Doppler radar in QPE, the newly formulated Z/R equations will be useful in improving the measurement of rainfall for any hydrological application, especially for flood forecasting.
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: The UML modeling of complex distributed systems often is a great challenge due to the large amount of parallel real-time operating components. In this paper the problems of verification of such systems are discussed. ECPN, an Extended Colored Petri Net is defined to formally describe state transitions of components and interactions among components. The relationship between sequence diagrams and Free Choice Petri Nets is investigated. Free Choice Petri Net theory helps verifying the liveness of sequence diagrams. By converting sequence diagrams to ECPNs and then comparing behaviors of sequence diagram ECPNs and statecharts, the consistency among models is analyzed. Finally, a verification process for an example model is demonstrated.
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: In the present study Schwertmannite (an iron oxide
hydroxide) is selected as an adsorbent for defluoridation of water.
The adsorbent was prepared by wet chemical process and was
characterized by SEM, XRD and BET. The fluoride adsorption
efficiency of the prepared adsorbent was determined with respect to
contact time, initial fluoride concentration, adsorbent dose and pH of
the solution. The batch adsorption data revealed that the fluoride
adsorption efficiency was highly influenced by the studied factors.
Equilibrium was attained within one hour of contact time indicating
fast kinetics and the adsorption data followed pseudo second order
kinetic model. Equilibrium isotherm data fitted to both Langmuir and
Freundlich isotherm models for a concentration range of 5-30 mg/L.
The adsorption system followed Langmuir isotherm model with
maximum adsorption capacity of 11.3 mg/g. The high adsorption
capacity of Schwertmannite points towards the potential of this
adsorbent for fluoride removal from aqueous medium.
Abstract: In this research, CaO-ZnO catalysts (with various
Ca:Zn atomic ratios of 1:5, 1:3, 1:1, and 3:1) prepared by incipientwetness
impregnation (IWI) and co-precipitation (CP) methods were
used as a catalyst in the transesterification of palm oil with methanol
for biodiesel production. The catalysts were characterized by several
techniques, including BET method, CO2-TPD, and Hemmett
Indicator. The effects of precursor concentration, and calcination
temperature on the catalytic performance were studied under reaction
conditions of a 15:1 methanol to oil molar ratio, 6 wt% catalyst,
reaction temperature of 60°C, and reaction time of 8 h. At Ca:Zn
atomic ratio of 1:3 gave the highest FAME value owing to a basic
properties and surface area of the prepared catalyst.