Abstract: Dielectric ceramic samples in the BaO-Re2O3-TiO2
ternary system were synthesized with structural formula Ba2-
xRe4+2x/3Ti8O24 where Re= rare earth metal and Re= Sm and La where
x varies from 0.0 to 0.6 with step size 0.1. Polycrystalline samples
were prepared by the conventional solid state reaction technique. The
dielectric, electrical and impedance analysis of all the samples in the
frequency range 1KHz- 1MHz at room temperature (25°C) have been
done to get the understanding of electrical conduction and dielectric
relaxation and their correlation. Dielectric response of the samples at
lower frequencies shows dielectric dispersion while at higher
frequencies it shows dielectric relaxation. The ac conductivity is well
fitted by the Jonscher law. The spectroscopic data in the impedance
plane confirms the existence of grain contribution to the relaxation.
All the properties are found out to be function of frequency as well as
the amount of substitution.
Abstract: This paper presents a rank correlation curve. The
traditional correlation coefficient is valid for both continuous
variables and for integer variables using rank statistics. Since
the correlation coefficient has already been established in rank
statistics by Spearman, such a calculation can be extended to
the correlation curve.
This paper presents two survey questions. The survey
collected non-continuous variables. We will show weak to
moderate correlation. Obviously, one question has a negative
effect on the other. A review of the qualitative literature
can answer which question and why. The rank correlation
curve shows which collection of responses has a positive
slope and which collection of responses has a negative slope.
Such information is unavailable from the flat, ”first-glance”
correlation statistics.
Abstract: The present study was undertaken to investigate the
effect of aging parameters (time and temperature) on the mechanical
properties of Be-and/or Zr- treated Al-Mg-Zn (7075) alloys. Ultimate
tensile strength, 0.5% offset yield strength and % elongation
measurements were carried out on specimens prepared from cast and
heat treated 7075 alloys containing Be and/or Zr. Different aging
treatment were carried out for the as solution treated (SHT)
specimens (after quenching in warm water). The specimens were
aged at different conditions; Natural and artificial aging was carried
out at room temperature, 120C, 150C, 180C and 220C for different
periods of time. Duplex aging was performed for SHT conditions
(pre-aged at different time and temperature followed by high
temperature aging). Ultimate tensile strength, yield strength and %
elongation data results as a function of different aging parameters are
analysed. A statistical design of experiments (DOE) approach using
fractional factorial design is applied to acquire an understanding of
the effects of these variables and their interactions on the mechanical
properties of Be- and/or Zr- treated 7075 alloys. Mathematical
models are developed to relate the alloy mechanical properties with
the different aging parameters.
Abstract: This paper proposes a GLMM with spatial and
temporal effects for malaria data in Thailand. A Bayesian method is
used for parameter estimation via Gibbs sampling MCMC. A
conditional autoregressive (CAR) model is assumed to present the
spatial effects. The temporal correlation is presented through the
covariance matrix of the random effects. The malaria quarterly data
have been extracted from the Bureau of Epidemiology, Ministry of
Public Health of Thailand. The factors considered are rainfall and
temperature. The result shows that rainfall and temperature are
positively related to the malaria morbidity rate. The posterior means
of the estimated morbidity rates are used to construct the malaria
maps. The top 5 highest morbidity rates (per 100,000 population) are
in Trat (Q3, 111.70), Chiang Mai (Q3, 104.70), Narathiwat (Q4,
97.69), Chiang Mai (Q2, 88.51), and Chanthaburi (Q3, 86.82).
According to the DIC criterion, the proposed model has a better
performance than the GLMM with spatial effects but without
temporal terms.
Abstract: Digital information is expanding in exponential order in our life. Information that is residing online and offline are stored in huge repositories relating to every aspect of our lives. Getting the required information is a task of retrieval systems. Content based image retrieval (CBIR) is a retrieval system that retrieves the required information from repositories on the basis of the contents of the image. Time is a critical factor in retrieval system and using indexed views with CBIR system improves the time efficiency of retrieved results.
Abstract: This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each
state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise
ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.
Abstract: An empirical correlation for predicting the heat transfer coefficient for a cylinder under free convection, inclined at any arbitrary angle with the horizontal has been developed in terms of Nusselt number, Prandtl number and Grashof number. Available experimental data was used to determine the parameters for the proposed correlation. The proposed correlation predicts the available data well within ±10%, for Prandtl number in the range 0.68-0.72 and Grashof number in the range 1.4×104–1.2×1010.
Abstract: This study was carried out to assess rural women’s skill acquisition in the processing of locust bean in Ipokia Local Government Area of Ogun State, Nigeria. Simple random sampling technique was used to select 90 women locust bean processors for this study. Data were analyzed with descriptive statistics and Pearson Product Moment Correlation. The result showed that the mean age of respondents was 40.72 years. Most (70.00%) of the respondents were married. The mean processing experience was 8.63 years. 93.30% of the respondents relied on information from fellow locust beans processors and friends. All (100%) the respondents did not acquire improved processing skill through trainings and workshops. It can be concluded that the rural women’s skill acquisition on modernized processing techniques was generally low. It is hereby recommend that the rural women processors should be trained by extension service providers through series of workshops and seminars on improved processing techniques.
Abstract: Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this work K-Means algorithms has been applied for clustering of Gene Expression Data. Further, rough set based Quick reduct algorithm has been applied for each cluster in order to select the most similar genes having high correlation. Then the ACV measure is used to evaluate the refined clusters and classification is used to evaluate the proposed method. They could identify compact clusters with feature selection method used to genes are selected.
Abstract: This study employs sampling of 400 Thai people who live in Bangkok and have used air transportation to travel. A random convenience sampling technique is used to collect data. The results found that at 0.05 significance level the differences of means of Thai consumers’ expectations (before) and satisfaction (after) receiving services in the service marketing mix, the results of all aspects are different both in general and for each aspect of the service marketing mix. Average levels of expectations before receiving services are higher than satisfaction after receiving services in all aspects, as well. When analyzing further to the correlation between average means, the means of expectations before receiving services are higher than those of satisfaction after receiving services in general. As in all aspects of the service marketing mix, any aspect that has a big difference between expectations before receiving services and satisfaction after receiving services has low correlation.
Abstract: The Tropical Data Hub (TDH) is a virtual research environment that provides researchers with an e-research infrastructure to congregate significant tropical data sets for data reuse, integration, searching, and correlation. However, researchers often require data and metadata synthesis across disciplines for cross-domain analyses and knowledge discovery. A triplestore offers a semantic layer to achieve a more intelligent method of search to support the synthesis requirements by automating latent linkages in the data and metadata. Presently, the benchmarks to aid the decision of which triplestore is best suited for use in an application environment like the TDH are limited to performance. This paper describes a new evaluation tool developed to analyze both features and performance. The tool comprises a weighted decision matrix to evaluate the interoperability, functionality, performance, and support availability of a range of integrated and native triplestores to rank them according to requirements of the TDH.
Abstract: Maximal length sequences (m-sequences) are also
known as pseudo random sequences or pseudo noise sequences
for closely following Golomb-s popular randomness properties: (P1)
balance, (P2) run, and (P3) ideal autocorrelation. Apart from these,
there also exist certain other less known properties of such sequences
all of which are discussed in this tutorial paper. Comprehensive proofs
to each of these properties are provided towards better understanding
of such sequences. A simple test is also proposed at the end of
the paper in order to distinguish pseudo noise sequences from truly
random sequences such as Bernoulli sequences.
Abstract: The structural interpretation of a part of eastern Potwar
(Missa Keswal) has been carried out with available seismological,
seismic and well data. Seismological data contains both the source
parameters and fault plane solution (FPS) parameters and seismic data
contains ten seismic lines that were re-interpreted by using well data.
Structural interpretation depicts two broad types of fault sets namely,
thrust and back thrust faults. These faults together give rise to pop up
structures in the study area and also responsible for many structural
traps and seismicity. Seismic interpretation includes time and depth
contour maps of Chorgali Formation while seismological interpretation
includes focal mechanism solution (FMS), depth, frequency,
magnitude bar graphs and renewal of Seismotectonic map. The Focal
Mechanism Solutions (FMS) that surrounds the study area are
correlated with the different geological and structural maps of the area
for the determination of the nature of subsurface faults. Results of
structural interpretation from both seismic and seismological data
show good correlation. It is hoped that the present work will help in
better understanding of the variations in the subsurface structure and
can be a useful tool for earthquake prediction, planning of oil field and
reservoir monitoring.
Abstract: The purpose of this research was to study the behavior
trend factors of consumers to roasted coffee at the petrol station on
the route of Rangsit to Nakhon Nayok. The research drew upon data
collected from the regular consumers of roasted coffee stands. The
majority of respondents was male, 33-39 years old, and holding a
bachelor degree. The majority of respondents considered themselves
private business proprietors or entrepreneurs and had a monthly
income of between 10,000-16,000 baht. The regular coffee
consumers spent a minimum coffee expense of between 45 and 300
baht per day. These consumers also displayed good attitude and good
motivation which can be ranked as very high. From the hypothesis
testing of the behavior trend for the roasted coffee consumers in
repurchasing coffee and recommended the coffee to others, the
findings revealed that it had a significant correlation. Moreover, the
overall attitude towards the marketing mix factors also had a
significant correlation with the behavior trend consumers.
Abstract: In this paper a new fast simplification method is
presented. Such method realizes Karnough map with large
number of variables. In order to accelerate the operation of the
proposed method, a new approach for fast detection of group
of ones is presented. Such approach implemented in the
frequency domain. The search operation relies on performing
cross correlation in the frequency domain rather than time one.
It is proved mathematically and practically that the number of
computation steps required for the presented method is less
than that needed by conventional cross correlation. Simulation
results using MATLAB confirm the theoretical computations.
Furthermore, a powerful solution for realization of complex
functions is given. The simplified functions are implemented
by using a new desigen for neural networks. Neural networks
are used because they are fault tolerance and as a result they
can recognize signals even with noise or distortion. This is
very useful for logic functions used in data and computer
communications. Moreover, the implemented functions are
realized with minimum amount of components. This is done
by using modular neural nets (MNNs) that divide the input
space into several homogenous regions. Such approach is
applied to implement XOR function, 16 logic functions on one
bit level, and 2-bit digital multiplier. Compared to previous
non- modular designs, a clear reduction in the order of
computations and hardware requirements is achieved.
Abstract: This paper presents a distributed intrusion
detection system IDS, based on the concept of specialized
distributed agents community representing agents with the
same purpose for detecting distributed attacks. The semantic of
intrusion events occurring in a predetermined network has been
defined. The correlation rules referring the process which our
proposed IDS combines the captured events that is distributed
both spatially and temporally. And then the proposed IDS tries
to extract significant and broad patterns for set of well-known
attacks. The primary goal of our work is to provide intrusion
detection and real-time prevention capability against insider
attacks in distributed and fully automated environments.
Abstract: Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.
Abstract: The objectives of this research were to explore factors
influencing knowledge management process in the manufacturing
industry and develop a model to support knowledge management
processes. The studied factors were technology infrastructure, human
resource, knowledge sharing, and the culture of the organization. The
knowledge management processes included discovery, capture,
sharing, and application. Data were collected through questionnaires
and analyzed using multiple linear regression and multiple
correlation. The results found that technology infrastructure, human
resource, knowledge sharing, and culture of the organization
influenced the discovery and capture processes. However, knowledge
sharing had no influence in sharing and application processes. A
model to support knowledge management processes was developed,
which indicated that sharing knowledge needed further improvement
in the organization.
Abstract: The paper presents a one-dimensional transient
mathematical model of compressible non-isothermal multicomponent
fluid mixture flow in a pipe. The set of the mass,
momentum and enthalpy conservation equations for gas phase is
solved in the model. Thermo-physical properties of multi-component
gas mixture are calculated by solving the Equation of State (EOS)
model. The Soave-Redlich-Kwong (SRK-EOS) model is chosen. Gas
mixture viscosity is calculated on the basis of the Lee-Gonzales-
Eakin (LGE) correlation. Numerical analysis of rapid gas
decompression process in rich and base natural gases is made on the
basis of the proposed mathematical model. The model is successfully
validated on the experimental data [1]. The proposed mathematical
model shows a very good agreement with the experimental data [1] in
a wide range of pressure values and predicts the decompression in
rich and base gas mixtures much better than analytical and
mathematical models, which are available from the open source
literature.
Abstract: The passive electrical properties of a tissue depends
on the intrinsic constituents and its structure, therefore by measuring
the complex electrical impedance of the tissue it might be possible to
obtain indicators of the tissue state or physiological activity [1].
Complete bio-impedance information relative to physiology and
pathology of a human body and functional states of the body tissue or
organs can be extracted by using a technique containing a fourelectrode
measurement setup. This work presents the estimation
measurement setup based on the four-electrode technique. First, the
complex impedance is estimated by three different estimation
techniques: Fourier, Sine Correlation and Digital De-convolution and
then estimation errors for the magnitude, phase, reactance and
resistance are calculated and analyzed for different levels of
disturbances in the observations. The absolute values of relative
errors are plotted and the graphical performance of each technique is
compared.