Abstract: With the rapid growth and development of information and communication technology, the Internet has played a definite and irreplaceable role in people-s social lives in Taiwan like in other countries. In July 2008, on a general social website, an unexpected phenomenon was noticed – that there were more than one hundred users who started forming clubs voluntarily and having face-to-face gatherings for specific purposes. In this study, it-s argued whether or not teenagers- social contact on the Internet is involved in their life context, and tried to reveal the teenagers- social preferences, values, and needs, which merge with and influence teenagers- social activities. Therefore, the study conducts multiple user experience research methods, which include practical observations and qualitative analysis by contextual inquiries and in-depth interviews. Based on the findings, several design implications for software related to social interactions and cultural inheritance are offered. It is concluded that the inherent values of a social behaviors might be a key issue in developing computer-mediated communication or interaction designs in the future.
Abstract: In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on frequency density based partitioning of the historical enrollment data. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting enrollments than the existing methods.
Abstract: Various formal and informal brand alliances are being formed in professional service firms. Professional service corporate brand is heavily dependent on brands of professional employees who comprise them, and professional employee brands are in turn dependent on the corporate brand. Prior work provides limited scientific evidence of brand alliance effects in professional service area – i.e., how professional service corporate-employee brand allies are affected by an alliance, what are brand attitude effects after alliance formation and how these effects vary with different strengths of an ally. Scientific literature analysis and theoretical modeling are the main methods of the current study. As a result, a theoretical model is constructed for estimating spillover effects of professional service corporate-employee brand alliances and for comparison among different professional service firm expertise practice models – from “brains" to “procedure" model. The resulting theoretical model lays basis for future experimental studies.
Abstract: Glutathione S-transferase was purified from human
erythrocytes and effects of some polyphenols were investigated on
the enzyme activity. The purification procedure was performed on
Glutathione-Agarose affinity chromatography after preparation of
erythrocytes hemolysate with a yield of 81%. The purified enzyme
showed a single band on the SDS-PAGE. The effects of some
poliphenolic compounds such as catechin, dopa, dopamine, progallol
and catechol were examined on the in vitro GST activity. Catechin
was determined to be inhibitor for the enzyme, but others were not
effective on the enzyme as inhibitors or activators. IC50 value -the
concentration of inhibitor which reduces enzyme activity by 50%-
was estimated to be 10 mM. Ki constants were also calculated as 6.38
± 0,70 mM with GSH substrate, and 3.86 ± 0,78 mM with CDNB
substrate using the equations of graphs for the inhibitor, and its
inhibition type was determined as non-competitive.
Abstract: Changing in consumers lifestyles and food
consumption patterns provide a great opportunity in developing the
functional food sector in Malaysia. There is only a little knowledge
about whether Malaysian consumers are aware of functional food and
if so what image consumers have of this product. The objective of
this research is to determine the extent to which selected socioeconomic
characteristics and attitudes influence consumers-
awareness of functional food. A survey was conducted in the Klang
Valley, Malaysia where 439 respondents were interviewed using a
structured questionnaire. The result shows that most respondents
have a positive attitude towards functional food. For the binary
logistic estimation, the results indicate that age, income and other
factors such as concern about food safety, subscribing to cooking or
health magazines, being a vegetarian and consumers who have been
involved in a food production company significantly influence
Malaysian consumers- awareness towards functional food.
Abstract: The classification of the protein structure is commonly
not performed for the whole protein but for structural domains, i.e.,
compact functional units preserved during evolution. Hence, a first
step to a protein structure classification is the separation of the
protein into its domains. We approach the problem of protein domain
identification by proposing a novel graph theoretical algorithm. We
represent the protein structure as an undirected, unweighted and
unlabeled graph which nodes correspond the secondary structure
elements of the protein. This graph is call the protein graph. The
domains are then identified as partitions of the graph corresponding
to vertices sets obtained by the maximization of an objective function,
which mutually maximizes the cycle distributions found in the
partitions of the graph. Our algorithm does not utilize any other kind
of information besides the cycle-distribution to find the partitions. If
a partition is found, the algorithm is iteratively applied to each of
the resulting subgraphs. As stop criterion, we calculate numerically
a significance level which indicates the stability of the predicted
partition against a random rewiring of the protein graph. Hence,
our algorithm terminates automatically its iterative application. We
present results for one and two domain proteins and compare our
results with the manually assigned domains by the SCOP database
and differences are discussed.
Abstract: Volume rendering is widely used in medical CT image
visualization. Applying 3D image visualization to diagnosis
application can require accurate volume rendering with high
resolution. Interpolation is important in medical image processing
applications such as image compression or volume resampling.
However, it can distort the original image data because of edge
blurring or blocking effects when image enhancement procedures
were applied. In this paper, we proposed adaptive tension control
method exploiting gradient information to achieve high resolution
medical image enhancement in volume visualization, where restored
images are similar to original images as much as possible. The
experimental results show that the proposed method can improve
image quality associated with the adaptive tension control efficacy.
Abstract: The present work was conducted for the synthesis of
nano size zerovalent iron (nZVI) and hexavalent chromium (Cr(VI))
removal as a highly toxic pollutant by using this nanoparticles. Batch
experiments were performed to investigate the effects of Cr(VI),
nZVI concentration, pH of solution and contact time variation on
the removal efficiency of Cr(VI). nZVI was synthesized by
reduction of ferric chloride using sodium borohydrid. SEM and
XRD examinations applied for determination of particle size and
characterization of produced nanoparticles. The results showed that
the removal efficiency decreased with Cr(VI) concentration and pH
of solution and increased with adsorbent dosage and contact time.
The Langmuir and Freundlich isotherm models were used for the
adsorption equilibrium data and the Langmuir isotherm model was
well fitted. Nanoparticle ZVI presented an outstanding ability to
remove Cr(VI) due to high surface area, low particle size and high
inherent activity.
Abstract: Recommender Systems act as personalized decision
guides, aiding users in decisions on matters related to personal taste.
Most previous research on Recommender Systems has focused on the
statistical accuracy of the algorithms driving the systems, with no
emphasis on the trustworthiness of the user. RS depends on
information provided by different users to gather its knowledge. We
believe, if a large group of users provide wrong information it will
not be possible for the RS to arrive in an accurate conclusion. The
system described in this paper introduce the concept of Testing the
knowledge of user to filter out these “bad users".
This paper emphasizes on the mechanism used to provide robust
and effective recommendation.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: The aim of this contribution is to present a new
approach in modeling the electrical activity of the human heart. A
recurrent artificial neural network is being used in order to exhibit a
subset of the dynamics of the electrical behavior of the human heart.
The proposed model can also be used, when integrated, as a
diagnostic tool of the human heart system.
What makes this approach unique is the fact that every model is
being developed from physiological measurements of an individual.
This kind of approach is very difficult to apply successfully in many
modeling problems, because of the complexity and entropy of the
free variables describing the complex system. Differences between
the modeled variables and the variables of an individual, measured at
specific moments, can be used for diagnostic purposes. The sensor
fusion used in order to optimize the utilization of biomedical sensors
is another point that this paper focuses on. Sensor fusion has been
known for its advantages in applications such as control and
diagnostics of mechanical and chemical processes.
Abstract: Shape optimization of the airfoil with high aspect ratio
of long endurance unmanned aerial vehicle (UAV) is performed by the
multi-objective optimization technology coupled with computational
fluid dynamics (CFD). For predicting the aerodynamic characteristics
around the airfoil the high-fidelity Navier-Stokes solver is employed
and SMOGA (Simple Multi-Objective Genetic Algorithm), which is
developed by authors, is used for solving the multi-objective
optimization problem. To obtain the optimal solutions of the design
variable (i.e., sectional airfoil profile, wing taper ratio and sweep) for
high performance of UAVs, both the lift and lift-to-drag ratio are
maximized whereas the pitching moment should be minimized,
simultaneously. It is found that the lift force and lift-to-drag ratio are
linearly dependent and a unique and dominant solution are existed.
However, a trade-off phenomenon is observed between the lift-to-drag
ratio and pitching moment. As the result of optimization, sixty-five
(65) non-dominated Pareto individuals at the cutting edge of design
spaces that is decided by airfoil shapes can be obtained.
Abstract: Saffron (Crocus sativus) is cultivated as spices,
medicinal and aromatic plant species. At autumn season, heavy
rainfall can cause flooding stress and inhibits growth of saffron. Thus
this research was conducted to study the effect of silver ion (as an
ethylene inhibitor) on growth of saffron under flooding conditions.
The corms of saffron were soaked with one concentration of nano
silver (0, 40, 80 or 120 ppm) and then planting under flooding stress
or non flooding stress conditions. Results showed that number of
roots, root length, root fresh and dry weight, leaves fresh and dry
weight were reduced by 10 day flooding stress. Soaking saffron
corms with 40 or 80 ppm concentration of nano silver rewarded the
effect of flooding stress on the root number, by increasing it.
Furthermore, 40 ppm of nano silver increased root length in stress.
Nano silver 80 ppm in flooding stress, increased leaves dry weight.
Abstract: The purpose of this paper was to study motivation
factors affecting job performance effectiveness. This paper drew
upon data collected from an Internal Audit Staffs of Internal Audit
Line of Head Office of Krung Thai Public Company Limited.
Statistics used included frequency, percentage, mean and standard
deviation, t-test, and one-way ANOVA test. The finding revealed that
the majority of the respondents were female of 46 years of age and
over, married and live together, hold a bachelor degree, with an
average monthly income over 70,001 Baht. The majority of
respondents had over 15 years of work experience. They generally
had high working motivation as well as high job performance
effectiveness.
The hypotheses testing disclosed that employees with different
working status had different level of job performance effectiveness at
a 0.01 level of significance. Working motivation factors had an effect
on job performance in the same direction with high level. Individual
working motivation included working completion, reorganization,
working progression, working characteristic, opportunity,
responsibility, management policy, supervision, relationship with
their superior, relationship with co-worker, working position,
working stability, safety, privacy, working conditions, and payment.
All of these factors related to job performance effectiveness in the
same direction with medium level.
Abstract: Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.
Abstract: This paper presents a supervised clustering algorithm,
namely Grid-Based Supervised Clustering (GBSC), which is able to
identify clusters of any shapes and sizes without presuming any
canonical form for data distribution. The GBSC needs no prespecified
number of clusters, is insensitive to the order of the input
data objects, and is capable of handling outliers. Built on the
combination of grid-based clustering and density-based clustering,
under the assistance of the downward closure property of density
used in bottom-up subspace clustering, the GBSC can notably reduce
its search space to avoid the memory confinement situation during its
execution. On two-dimension synthetic datasets, the GBSC can
identify clusters with different shapes and sizes correctly. The GBSC
also outperforms other five supervised clustering algorithms when
the experiments are performed on some UCI datasets.
Abstract: In this paper, a robust watermarking algorithm using
the wavelet transform and edge detection is presented. The efficiency
of an image watermarking technique depends on the preservation of
visually significant information. This is attained by embedding the
watermark transparently with the maximum possible strength. The
watermark embedding process is carried over the subband
coefficients that lie on edges, where distortions are less noticeable,
with a subband level dependent strength. Also, the watermark is
embedded to selected coefficients around edges, using a different
scale factor for watermark strength, that are captured by a
morphological dilation operation. The experimental evaluation of the
proposed method shows very good results in terms of robustness and
transparency to various attacks such as median filtering, Gaussian
noise, JPEG compression and geometrical transformations.
Abstract: Trihalogenmethanes are the most significant byproducts of the reaction of disinfection agent with organic precursors naturally present in ground and surface waters.Their incidence negatively affects the quality of drinking water in relation to their nephrotoxic, hepatotoxic and genotoxic effects on human health. Taking into consideration the considerable volatility of monitored contaminants it could be assumed that their incidence in drinking water would depend on the distance of sampling from the area of disinfection. Based on the concentration of trihalogenmethanes determined with the help of gas chromatography with mass detector and the analysis of variance (ANOVA) such dependence has been proved as statistically significant. The acquired outcomes will be used for assessing the non-carcinogenic and genotoxic risks to consumers.
Abstract: Following the laser ablation studies leading to a
theory of nuclei confinement by a Debye layer mechanism, we
present here numerical evaluations for the known stable nuclei where
the Coulomb repulsion is included as a rather minor component
especially for lager nuclei. In this research paper the required
physical conditions for the formation and stability of nuclei
particularly endothermic nuclei with mass number greater than to
which is an open astrophysical question have been investigated.
Using the Debye layer mechanism, nuclear surface energy, Fermi
energy and coulomb repulsion energy it is possible to find conditions
under which the process of nucleation is permitted in early universe.
Our numerical calculations indicate that about 200 second after the
big bang at temperature of about 100 KeV and subrelativistic region
with nucleon density nearly equal to normal nuclear density namely,
10cm all endothermic and exothermic nuclei have been
formed.
Abstract: In this paper, the 1-D conduction-radiation problem is solved by the lattice Boltzmann method. The effects of various parameters such as the scattering albedo, the conduction–radiation parameter and the wall emissivity are studied. In order to check on the accuracy of the numerical technique employed for the solution of the considered problem, the present numerical code was validated with the published study. The found results are in good agreement with those published