Abstract: Benchmarking cleaner production performance is an
effective way of pollution control and emission reduction in coal-fired
power industry. A benchmarking method using two-stage
super-efficiency data envelopment analysis for coal-fired power plants
is proposed – firstly, to improve the cleaner production performance of
DEA-inefficient or weakly DEA-efficient plants, then to select the
benchmark from performance-improved power plants. An empirical
study is carried out with the survey data of 24 coal-fired power plants.
The result shows that in the first stage the performance of 16 plants is
DEA-efficient and that of 8 plants is relatively inefficient. The target
values for improving DEA-inefficient plants are acquired by
projection analysis. The efficient performance of 24 power plants and
the benchmarking plant is achieved in the second stage. The two-stage
benchmarking method is practical to select the optimal benchmark in
the cleaner production of coal-fired power industry and will
continuously improve plants- cleaner production performance.
Abstract: The Reverse Monte Carlo (RMC) simulation is applied in the study of an aqueous electrolyte LiCl6H2O. On the basis of the available experimental neutron scattering data, RMC computes pair radial distribution functions in order to explore the structural features of the system. The obtained results include some unrealistic features. To overcome this problem, we use the Hybrid Reverse Monte Carlo (HRMC), incorporating an energy constraint in addition to the commonly used constraints derived from experimental data. Our results show a good agreement between experimental and computed partial distribution functions (PDFs) as well as a significant improvement in pair partial distribution curves. This kind of study can be considered as a useful test for a defined interaction model for conventional simulation techniques.
Abstract: Many real-world data sets consist of a very high dimensional feature space. Most clustering techniques use the distance or similarity between objects as a measure to build clusters. But in high dimensional spaces, distances between points become relatively uniform. In such cases, density based approaches may give better results. Subspace Clustering algorithms automatically identify lower dimensional subspaces of the higher dimensional feature space in which clusters exist. In this paper, we propose a new clustering algorithm, ISC – Intelligent Subspace Clustering, which tries to overcome three major limitations of the existing state-of-art techniques. ISC determines the input parameter such as є – distance at various levels of Subspace Clustering which helps in finding meaningful clusters. The uniform parameters approach is not suitable for different kind of databases. ISC implements dynamic and adaptive determination of Meaningful clustering parameters based on hierarchical filtering approach. Third and most important feature of ISC is the ability of incremental learning and dynamic inclusion and exclusions of subspaces which lead to better cluster formation.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Abstract: Mining frequent tree patterns have many useful
applications in XML mining, bioinformatics, network routing, etc.
Most of the frequent subtree mining algorithms (i.e. FREQT,
TreeMiner and CMTreeMiner) use anti-monotone property in the
phase of candidate subtree generation. However, none of these
algorithms have verified the correctness of this property in tree
structured data. In this research it is shown that anti-monotonicity
does not generally hold, when using weighed support in tree pattern
discovery. As a result, tree mining algorithms that are based on this
property would probably miss some of the valid frequent subtree
patterns in a collection of trees. In this paper, we investigate the
correctness of anti-monotone property for the problem of weighted
frequent subtree mining. In addition we propose W3-Miner, a new
algorithm for full extraction of frequent subtrees. The experimental
results confirm that W3-Miner finds some frequent subtrees that the
previously proposed algorithms are not able to discover.
Abstract: There is strong evidence that water channel proteins
'aquaporins (AQPs)' are central components in plant-water relations
as well as a number of other physiological parameters. We had
previously reported the isolation of 24 plasma membrane intrinsic
protein (PIP) type AQPs. However, the gene numbers in rice and the
polyploid nature of bread wheat indicated a high probability of
further genes in the latter. The present work focused on identification
of further AQP isoforms in bread wheat. With the use of altered
primer design, we identified five genes homologous, designated
PIP1;5b, PIP2;9b, TaPIP2;2, TaPIP2;2a, TaPIP2;2b. Sequence
alignments indicate PIP1;5b, PIP2;9b are likely to be homeologues of
two previously reported genes while the other three are new genes
and could be homeologs of each other. The results indicate further
AQP diversity in wheat and the sequence data will enable physical
mapping of these genes to identify their genomes as well as genetic to
determine their association with any quantitative trait loci (QTLs)
associated with plant-water relation such as salinity or drought
tolerance.
Abstract: In this paper, the melting of a semi-infinite body as a
result of a moving laser beam has been studied. Because the Fourier
heat transfer equation at short times and large dimensions does not
have sufficient accuracy; a non-Fourier form of heat transfer
equation has been used. Due to the fact that the beam is moving in x
direction, the temperature distribution and the melting pool shape are
not asymmetric. As a result, the problem is a transient threedimensional
problem. Therefore, thermophysical properties such as
heat conductivity coefficient, density and heat capacity are functions
of temperature and material states. The enthalpy technique, used for
the solution of phase change problems, has been used in an explicit
finite volume form for the hyperbolic heat transfer equation. This
technique has been used to calculate the transient temperature
distribution in the semi-infinite body and the growth rate of the melt
pool. In order to validate the numerical results, comparisons were
made with experimental data. Finally, the results of this paper were
compared with similar problem that has used the Fourier theory. The
comparison shows the influence of infinite speed of heat propagation
in Fourier theory on the temperature distribution and the melt pool
size.
Abstract: We present a new algorithm for nonlinear dimensionality reduction that consistently uses global information, and that enables understanding the intrinsic geometry of non-convex manifolds. Compared to methods that consider only local information, our method appears to be more robust to noise. Unlike most methods that incorporate global information, the proposed approach automatically handles non-convexity of the data manifold. We demonstrate the performance of our algorithm and compare it to state-of-the-art methods on synthetic as well as real data.
Abstract: We decribe a formal specification and verification of the Rabin public-key scheme in the formal proof system Is-abelle/HOL. The idea is to use the two views of cryptographic verification: the computational approach relying on the vocabulary of probability theory and complexity theory and the formal approach based on ideas and techniques from logic and programming languages. The analysis presented uses a given database to prove formal properties of our implemented functions with computer support. Thema in task in designing a practical formalization of correctness as well as security properties is to cope with the complexity of cryptographic proving. We reduce this complexity by exploring a light-weight formalization that enables both appropriate formal definitions as well as eficient formal proofs. This yields the first computer-proved implementation of the Rabin public-key scheme in Isabelle/HOL. Consequently, we get reliable proofs with a minimal error rate augmenting the used database. This provides a formal basis for more computer proof constructions in this area.
Abstract: Diagnostic and detection of the arterial stiffness is
very important; which gives indication of the associated increased risk of cardiovascular diseases. To make a cheap and easy method for general screening technique to avoid the future cardiovascular
complexes , due to the rising of the arterial stiffness ; a proposed algorithm depending on photoplethysmogram to be used. The
photoplethysmograph signals would be processed in MATLAB. The
signal will be filtered, baseline wandering removed, peaks and
valleys detected and normalization of the signals should be achieved
.The area under the catacrotic phase of the photoplethysmogram
pulse curve is calculated using trapezoidal algorithm ; then will used
in cooperation with other parameters such as age, height, blood
pressure in neural network for arterial stiffness detection. The Neural
network were implemented with sensitivity of 80%, accuracy 85%
and specificity of 90% were got from the patients data. It is
concluded that neural network can detect the arterial STIFFNESS
depending on risk factor parameters.
Abstract: The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.
Abstract: Identifying parameters in an epidemic model is one
of the important aspect of modeling. In this paper, we suggest a
method to identify the transmission rate by using the multistage
Adomian decomposition method. As a case study, we use the data of
the reported dengue fever cases in the city of Shah Alam, Malaysia.
The result obtained fairly represents the actual situation. However, in
the SIR model, this method serves as an alternative in parameter
identification and enables us to make necessary analysis for a smaller
interval.
Abstract: In this paper we are to find the optimum
multiwavelet for compression of electrocardiogram (ECG)
signals. At present, it is not well known which multiwavelet is
the best choice for optimum compression of ECG. In this
work, we examine different multiwavelets on 24 sets of ECG
data with entirely different characteristics, selected from MITBIH
database. For assessing the functionality of the different
multiwavelets in compressing ECG signals, in addition to
known factors such as Compression Ratio (CR), Percent Root
Difference (PRD), Distortion (D), Root Mean Square Error
(RMSE) in compression literature, we also employed the
Cross Correlation (CC) criterion for studying the
morphological relations between the reconstructed and the
original ECG signal and Signal to reconstruction Noise Ratio
(SNR). The simulation results show that the cardbal2 by the
means of identity (Id) prefiltering method to be the best
effective transformation.
Abstract: The use of statistical data and of the neural networks, capable of elaborate a series of data and territorial info, have allowed the making of a model useful in the subdivision of urban places into homogeneous zone under the profile of a social, real estate, environmental and urbanist background of a city. The development of homogeneous zone has fiscal and urbanist advantages. The tools in the model proposed, able to be adapted to the dynamic changes of the city, allow the application of the zoning fast and dynamic.
Abstract: The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave's frequencies, while FFT is implemented to visualize the EEG waves in multi-resolution of DWT. Several real EEG data sets (real EEG data for both normal and abnormal persons) have been tested and the results improve the validity of the proposed technique.
Abstract: The objective of this research was to study the career
success and the satisfaction of female executives working for schools
in Bangkok, Thailand. This paper drew upon the survey data
collected from 68 female executives. The survey conducted in on
Bangkok schools. The statistics utilized in this paper included
percentage, mean, standard deviation as well as t-test. The findings
revealed that the majority of samples had more than 30 years of
experience, held a master degree, and had an average income of less
than 40,000 baht. The majority of respondents worked not more than
50 hours per week. In addition, the mean score revealed career
development was ranked as their number one career satisfaction and
having a job related to education was ranked as their number one job
satisfaction. Also, the mean score of all categories of satisfaction was
4.61 with standard deviation of 1.677 which indicated that female
executive level of satisfaction was high. In terms of the subjectivity
career success, the hypothesis testing’s result disclosed that female
executives with different married status had a difference in their job
satisfaction which was significant at the 0.05 confidence level.
Abstract: The electronically available Urdu data is in image form
which is very difficult to process. Printed Urdu data is the root cause
of problem. So for the rapid progress of Urdu language we need an
OCR systems, which can help us to make Urdu data available for the
common person. Research has been carried out for years to automata
Arabic and Urdu script. But the biggest hurdle in the development of
Urdu OCR is the challenge to recognize Nastalique Script which is
taken as standard for writing Urdu language. Nastalique script is
written diagonally with no fixed baseline which makes the script
somewhat complex. Overlap is present not only in characters but in
the ligatures as well. This paper proposes a method which allows
successful recognition of Nastalique Script.
Abstract: Estimation of stature is an important step in developing a biological profile for human identification. It may provide a valuable indicator for unknown individual in a population. The aim of this study was to analyses the relationship between stature and lower limb dimensions in the Malaysian population. The sample comprised 100 corpses, which included 69 males and 31 females between age ranges of 20 to 90 years old. The parameters measured were stature, thigh length, lower leg length, leg length, foot length, foot height and foot breadth. Results showed that mean values in males were significantly higher than those in females (P < 0.05). There were significant correlations between lower limb dimensions and stature. Cross-validation of the equation on 100 individuals showed close approximation between known stature and estimated stature. It was concluded that lower limb dimensions were useful for estimation of stature, which should be validated in future studies.
Abstract: A suitable e-learning system management needs to
carry out a web-information system in order to allow integrated
fruition of data and metadata concerning the activities typical of elearning
environment. The definition of a “web information system"
for e-learning takes advantage of the potentialities of Web
technologies both as for the access to metadata present on the several
platforms, and as for the implementation of courseware which make
up the relative didactic environment. What information systems have
in common is the technological environment on which they are
generally implemented and the use of metadata in order to structure
information at all cognitive and organization levels. In this work we
are going to define a methodology for the implementation of a
specific web information system for an e-learning environment.
Abstract: Does education matter to the quality of our life? The results of extensive studies offer an affirmative answer to this question: high education levels are positively associated with higher income, with more highly qualified professions, with lower risk of unemployment, with better physical health and also, it is said, with more happiness. However, exploring these relationships is far from straightforward. Aside from educational credentials, what properties distinguish functionally literate individuals? How can their personal level of satisfaction be measured? What are the social mechanisms whereby education affects well-being?Using a literacy index and several measures for well-being developed by secondary analysis of the Adult Literacy and Life Skills Survey database, this investigation examined the relationship between literacy skills and subjective wellbeing in several OECD (Organisation for Economic Co-operation and Development) countries. Special attention was been addressed to Italy, and in particular to two regions representing territorial differences in this country: Piedmont and Campania.