Abstract: This research study is an exploration of the selfdirected
professional development of teachers who teach in public
schools in an era of democracy and educational change in South
Africa. Amidst an ever-changing educational system, the teachers in
this study position themselves as self-directed teacher-learners where
they adopt particular learning practices which enable change within
the broader discourses of public schooling. Life-story interviews
were used to enter into the private and public spaces of five teachers
which offer glimpses of how particular systems shaped their
identities, and how the meanings of self-directed teacher-learner
shaped their learning practices. Through the Multidimensional
Framework of Analysis and Interpretation the teachers’ stories were
analysed through three lenses: restorying the field texts - the self
through story; the teacher-learner in relation to social contexts, and
practices of self-directed learning. This study shows that as teacherlearners
learn for change through self-directed learning practices,
they develop their agency as transformative intellectuals, which is
necessary for the reworking of South African public schools.
Abstract: In this paper we describe the Levenvberg-Marquardt
(LM) algorithm for identification and equalization of CDMA
signals received by an antenna array in communication channels.
The synthesis explains the digital separation and equalization of
signals after propagation through multipath generating intersymbol
interference (ISI). Exploiting discrete data transmitted and three
diversities induced at the reception, the problem can be composed
by the Block Component Decomposition (BCD) of a tensor of
order 3 which is a new tensor decomposition generalizing the
PARAFAC decomposition. We optimize the BCD decomposition by
Levenvberg-Marquardt method gives encouraging results compared to
classical alternating least squares algorithm (ALS). In the equalization
part, we use the Minimum Mean Square Error (MMSE) to perform
the presented method. The simulation results using the LM algorithm
are important.
Abstract: In this paper, Least Mean Square (LMS) adaptive
noise reduction algorithm is proposed to enhance the speech signal
from the noisy speech. In this, the speech signal is enhanced by
varying the step size as the function of the input signal. Objective and
subjective measures are made under various noises for the proposed
and existing algorithms. From the experimental results, it is seen that
the proposed LMS adaptive noise reduction algorithm reduces Mean
square Error (MSE) and Log Spectral Distance (LSD) as compared to
that of the earlier methods under various noise conditions with
different input SNR levels. In addition, the proposed algorithm
increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR
improvement (ΔSNRseg) values; improves the Mean Opinion Score
(MOS) as compared to that of the various existing LMS adaptive
noise reduction algorithms. From these experimental results, it is
observed that the proposed LMS adaptive noise reduction algorithm
reduces the speech distortion and residual noise as compared to that
of the existing methods.
Abstract: In today’s highly competitive, dynamic and
technology driven business circumstances, marketers are under
steady pressure to deliver the best. Organizations are continuously
improving and upgrading themselves to meet customer expectations
and demands. Technology has not only changed the way in which
business is done in modern times but has also transformed the way to
reach out to target audience. Marketers have identified most recent
media options to communicate and convince potential customers.
Numerous scholars have studied the research domain of advertising
and have tried to recognize different measures of advertisement
effectiveness in context of various media. The objective of this paper
is to critically review accessible literature on advertisement
effectiveness in context of varied advertising media, recognize major
gaps in the literature and identify future research prospects on the
basis of critical analysis of literature.
Abstract: The present study involved analysis of certain
characteristics of the perennial ryegrass (Lolium perenne L.)
genotypes collected from the natural flora of Ankara, and explores a
correlation among them. In order to evaluate the plants for breeding
purpose as per Turkey's environmental conditions, the perennial
ryegrass plants were collected from natural pasture of Ankara in 2004
and were utilized for the study. Seeds of the collected plants were
sown in pots and seedlings were prepared in a greenhouse. In 2005,
the seedlings were transplanted at 50 × 50 cm2 intervals in
Randomized Complete Blocks Design in an experimental field. In
2007 and 2008, data were recorded from the observations and
measurements of 568 perennial ryegrasses. The plant characteristics,
which were investigated, included re-growth time in spring, color,
density, growth habit, tendency to form inflorescence, time of
inflorescence, plant height, length of upper internode, spike length,
leaf length, leaf width, leaf area, leaf shape, number of spikelets per
spike, seed yield per spike and 1000 grain weight and the correlation
analyses were made using this data. Correlation coefficients were
estimated between all paired combinations of the studied traits. The
yield components exhibited varying trends of association among
themselves. Seed yield per spike showed significant and positive
association with the number of spikelets per spike, 1000 grain weight,
plant height, length of upper internode, spike length, leaf length, leaf
width, leaf area and color, but significant and negative association
with the growth habit and re-growth time in spring.
Abstract: This study aimed at investigating whether the
functional brain networks constructed using the initial EEG (obtained
when patients first visited hospital) can be correlated with the
progression of cognitive decline calculated as the changes of
mini-mental state examination (MMSE) scores between the latest and
initial examinations. We integrated the time–frequency cross mutual
information (TFCMI) method to estimate the EEG functional
connectivity between cortical regions, and the network analysis based
on graph theory to investigate the organization of functional networks
in aMCI. Our finding suggested that higher integrated functional
network with sufficient connection strengths, dense connection
between local regions, and high network efficiency in processing
information at the initial stage may result in a better prognosis of the
subsequent cognitive functions for aMCI. In conclusion, the functional
connectivity can be a useful biomarker to assist in prediction of
cognitive declines in aMCI.
Abstract: The aim of this paper is to analyze the ability to
identify and acquire knowledge from external sources at the regional
level in the Czech Republic. The results show that the most important
sources of knowledge for innovative activities are sources within the
businesses themselves, followed by customers and suppliers.
Furthermore, the analysis of relationships between the objective of
the innovative activity and the ability to identify and acquire
knowledge implies that knowledge obtained from (1) customers aims
at replacing outdated products and increasing product quality; (2)
suppliers aims at increasing capacity and flexibility of production;
and (3) competing businesses aims at growing market share and
increasing the flexibility of production and services. Regions should
therefore direct their support especially into development and
strengthening of networks within the value chain.
Abstract: The main objective of this study was to identify
factors and conditions that motivated and encouraged students
towards the math class and the factors that made this class an
attractive and lovely one. To do this end, questionnaires consisting of
15 questions were distributed among 85 math teachers working in
schools of Zahedan. Having collected and reviewed these
questionnaires, it was shown that doing activity in math class
(activity of students while teaching) and previous math teachers'
behaviors have had much impact on encouraging the students
towards mathematics. Separation of educational classroom of
mathematics from the main classroom (which is decorated with crafts
created by students themselves with regard to math book including
article, wall newspaper, figures and formulas), peers, size and
appearance of math book, first grade teachers in each educational
level, among whom the Elementary first grade teachers had more
importance and impact, were among the most influential and
important factors in this regard. Then, school environment, family,
conducting research related to mathematics, its application in daily
life and other courses and studying the history of mathematics were
categorized as important factors that would increase the students’
interest in mathematics.
Abstract: This paper reports the empirical investigation on the
effect of involuntary displacement of indigenous tribes on their sociocultural
and food practices. A descriptive research design using the
quantitative approach was applied and individual of indigenous tribes
as unit of analysis. Through a self-administered survey among two
selected Malaysia indigenous tribes, one hundred fifty questionnaires
were successfully collected. With the application of descriptive and
inferential statistic some useful insights pertaining to the issue
investigated was significantly obtained. Findings revealed that
improvement on the socio-culture, economy and knowledge is
apparent on the indigenous groups’ resulted from displacement
program. Displacement also has a slight impact on indigenous
groups’ food practices. These positive indications provide significant
implications, not only for the indigenous groups themselves, but also
for the responsible authorities.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: In this paper, GSM signal strength was measured in
order to detect the type of the signal fading phenomenon using onedimensional
multilevel wavelet residual method and neural network
clustering to determine the average GSM signal strength received in
the study area. The wavelet residual method predicted that the GSM
signal experienced slow fading and attenuated with MSE of 3.875dB.
The neural network clustering revealed that mostly -75dB, -85dB and
-95dB were received. This means that the signal strength received in
the study is a weak signal.
Abstract: The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
Abstract: This research aims to study tourism data and behavior
of foreign tourists visited Wat Phrachetuponwimolmangkalaram (Wat
Po) Sample groups are tourists who visited inside the temple, during
February, March, April and May 2013. Tools used in the research are
questionnaires constructed by the researcher, and samples are dawn
by Convenience sampling. There are 207 foreign tourists who are
willing to be respondents. Statistics used are percentage, average
mean and standard deviation.
The results of the research reveal that:
A. General Data of Respondents
The foreign tourists who visited the temple are mostly female
(57.5 %), most respondents are aged between 20-29 years (37.2%).
Most respondents live in Europe (62.3%), most of them got the
Bachelor’s degree (40.1%), British are mostly found (16.4%),
respondents who are students are also found (23.2%), and Christian
are mostly found (60.9%).
B. Tourists’ Behavior While Visiting the Temple Compound.
The result shows that the respondents came with family (46.4%),
have never visited the temples (40.6%), and visited once (42 %). It is
found that the foreign tourists’ inappropriate behavior are wearing
revealing attires (58.9%), touching or getting closed to the monks
(55.1%), and speaking loudly (46.9%) respectively.
The respondents’ outstanding objectives are to visit inside the
temple (57.5%), to pay respect to the Reclining Buddha Image in the
Viharn (44.4%) and to worship the Buddha image in the Phra Ubosod
(37.7%) respectively.
C. The Respondents’ Self-evaluation of Performance
It is found that over all tourists evaluated themselves in the highest
level averaged 4.40. When focusing on each item, it is shown that
they evaluated themselves in the highest level on obeying the temple
staff averaged 4.57, and cleanness concern of the temple averaged
4.52, well-behaved performance during the temple visit averaged
4.47 respectively.
Abstract: The rapid development and growth of technology has changed the method of obtaining information for educators and learners. Technology has created a new world of collaboration and communication among people. Incorporating new technology into the teaching process can enhance learning outcomes. Billions of individuals across the world are now connected together, and are cooperating and contributing their knowledge and intelligence. Time is no longer wasted in waiting until the teacher is ready to share information as learners can go online and get it immediatelt.
The objectives of this paper are to understand the reasons why changes in teaching and learning methods are necessary, to find ways of improving them, and to investigate the challenges that present themselves in the adoption of new ICT tools in higher education institutes.
To achieve these objectives two primary research methods were used: questionnaires, which were distributed among students at higher educational institutes and multiple interviews with faculty members (teachers) from different colleges and universities, which were conducted to find out why teaching and learning methodology should change.
The findings show that both learners and educators agree that educational technology plays a significant role in enhancing instructors’ teaching style and students’ overall learning experience; however, time constraints, privacy issues, and not being provided with enough up-to-date technology do create some challenges.
Abstract: The acidity (citric acid) is the one of chemical content that can be refer to the internal quality and it’s a maturity index of tomato, The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR) spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomato.
Abstract: A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates.On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: Social media refers to the means of interactions
among people in which they create share, exchange and comment
contents among themselves in virtual communities and networks.
Social media or "social networking" has almost become part of our
daily lives and being tossed around over the past few years. It is like
any other media such as newspaper, radio and television but it is far
more than just about sharing information and ideas. Social
networking tools like Twitter, Facebook, Flickr and Blogs have
facilitated creation and exchange of ideas so quickly and widely than
the conventional media. This paper shows the choices,
communication, feeling comfort, time saving and effects of social
media among the people.
Abstract: The aims of this research are to broaden the study on the relationship between emotional intelligence and counterproductive work behavior (CWB). The study sample consisted in 441 Romanian employees from companies all over the country. Data has been collected through web surveys and processed with SPSS. The results indicated an average correlation between the two constructs and their sub variables, employees with a high level of emotional intelligence tend to be less aggressive. In addition, labeling was considered an individual difference which has the power to influence the level of employee aggression. A regression model was used to underline the importance of emotional intelligence together with labeling as predictors of CWB. Results have shown that this regression model enforces the assumption that labeling and emotional intelligence, taken together, predict CWB. Employees, who label themselves as victims and have a low degree of emotional intelligence, have a higher level of CWB.
Abstract: The purposes of this research were to develop and to
monitor the antecedent factors which directly affected the success
rate of new product development. This was a case study of the leather
industry in Bangkok, Thailand. A total of 350 leather factories were
used as a sample group. The findings revealed that the new product
development model was harmonized with the empirical data at the
acceptable level, the statistic values are: χ2=6.45, df= 7, p-value =
.48856; RMSEA = .000; RMR = .0029; AGFI = .98; GFI = 1.00. The
independent variable that directly influenced the dependent variable
at the highest level was marketing outcome which had a influence
coefficient at 0.32 and the independent variables that indirectly
influenced the dependent variables at the highest level was a clear
organization policy which had a influence coefficient at 0.17,
whereas, all independent variables can predict the model at 48
percent.