Abstract: Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
Abstract: Fruits and vegetables are the essentials of a healthy
diet, mainly because of their antioxidant properties contributing to
disease blockage especially for some certain types of cancer. Being a
favourite fruit, citrus are produced for economic and commercial
purposes worldwide. Particularly, lemon fruit (Citrus limon L.), has
an important place in export products of Turkey. Lemon has a great
importance on human nutrition with regard to being a source of
nutrients, flavonoids, vitamin C and minerals. It is used for food
flavouring and pickling and also processed for lemonade. By
processing citrus into fruit juices, consumption may increase and also
become easier. Like many fruits and vegetables lemons are cheap and
abundant during harvesting period, while they are quite expensive in
other seasons. Lemon juice and concentrate production allows
consumers to get benefits from lemon fruit in any time of the year.
Lemonade is getting in to the focus of consumers’ attention
preferring non-carbonated drinks. The demand of healthy, convenient
functional foods affects consumer trends through innovative
products. For this reason, lemonade could be enriched with different
natural herb extracts such as ginger (Zingiber officinale), linden (Tilia
cordata), and mint (Mentha piperita).
Abstract: Names are important in many societies, even in technologically oriented ones which use e.g. ID systems to identify individual people. Names such as surnames are the most important as they are used in many processes, such as identifying of people and genealogical research. On the other hand variation of names can be a major problem for the identification and search for people, e.g. web search or security reasons. Name matching presumes a-priori that the recorded name written in one alphabet reflects the phonetic identity of two samples or some transcription error in copying a previously recorded name. We add to this the lode that the two names imply the same person. This paper describes name variations and some basic description of various name matching algorithms developed to overcome name variation and to find reasonable variants of names which can be used to further increasing mismatches for record linkage and name search. The implementation contains algorithms for computing a range of fuzzy matching based on different types of algorithms, e.g. composite and hybrid methods and allowing us to test and measure algorithms for accuracy. NYSIIS, LIG2 and Phonex have been shown to perform well and provided sufficient flexibility to be included in the linkage/matching process for optimising name searching.
Abstract: Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.
Abstract: The feature of HIV genome is in a wide range because
of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point,
R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to
classify by using the coreceptors of HIV genome.
The aim of this study is to develop the optimal Multilayer
Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer
was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the
signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model
(AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4
viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.
Abstract: This work will provide a new perspective of exploring innovation thematic. It will reveal that radical and incremental innovations are complementary during the innovation life cycle and accomplished through distinct ways of developing new products. Each new product development process will be constructed according to the nature of each innovation and the state of the product development. This paper proposes the inclusion of the organizational function areas that influence new product's development on the new product development process.
Abstract: The objective of the research was to study of foot
anthropometry of children aged 7-12 years in the South of Thailand Thirty-three dimensions were measured on 305 male and 295 female
subjects with 3 age ranges (7-12 years old). The instrumentation consists of four types of anthropometer, digital vernier caliper, digital
height gauge and measuring tape. The mean values and standard
deviations of average age, height, and weight of the male subjects were 9.52(±1.70) years, 137.80(±11.55) cm, and 37.57(±11.65) kg.
Female average age, height, and weight subjects were 9.53(±1.70) years, 137.88(±11.55) cm, and 34.90(±11.57) kg respectively. The
comparison of the 33 comparison measured anthropometric. Between
male and female subjects were sexual differences in size on women in almost all areas of significance (p
Abstract: This paper presents an efficient emission constrained
economic dispatch algorithm that deals with nonlinear cost function
and constraints. It is then incorporated into the dynamic
programming based hydrothermal coordination program. The
program has been tested on a practical utility system having 32
thermal and 12 hydro generating units. Test results show that a slight
increase in production cost causes a substantial reduction in
emission.
Abstract: Sharing consistent and correct master data among
disparate applications in a reverse-logistics chain has long been
recognized as an intricate problem. Although a master data
management (MDM) system can surely assume that responsibility,
applications that need to co-operate with it must comply with
proprietary query interfaces provided by the specific MDM system. In
this paper, we present a RFID-ready MDM system which makes
master data readily available for any participating applications in a
reverse-logistics chain. We propose a RFID-wrapper as a part of our
MDM. It acts as a gateway between any data retrieval request and
query interfaces that process it. With the RFID-wrapper, any
participating applications in a reverse-logistics chain can easily
retrieve master data in a way that is analogous to retrieval of any other
RFID-based logistics transactional data.
Abstract: Wind energy has been shown to be one of the most
viable sources of renewable energy. With current technology, the low
cost of wind energy is competitive with more conventional sources of
energy such as coal. Most blades available for commercial grade
wind turbines incorporate a straight span-wise profile and airfoil
shaped cross sections. These blades are found to be very efficient at
lower wind speeds in comparison to the potential energy that can be
extracted. However as the oncoming wind speed increases the
efficiency of the blades decreases as they approach a stall point. This
paper explores the possibility of increasing the efficiency of the
blades at higher wind speeds while maintaining efficiency at the
lower wind speeds. The design intends to maintain efficiency at
lower wind speeds by selecting the appropriate orientation and size
of the airfoil cross sections based on a low oncoming wind speed and
given constant rotation rate. The blades will be made more efficient
at higher wind speeds by implementing a swept blade profile.
Performance was investigated using the computational fluid
dynamics (CFD).
Abstract: Nowadays, the increase of human population every
year results in increasing of water usage and demand. Saen Saep
canal is important canal in Bangkok. The main objective of this study
is using Artificial Neural Network (ANN) model to estimate the
Chemical Oxygen Demand (COD) on data from 11 sampling sites.
The data is obtained from the Department of Drainage and Sewerage,
Bangkok Metropolitan Administration, during 2007-2011. The
twelve parameters of water quality are used as the input of the
models. These water quality indices affect the COD. The
experimental results indicate that the ANN model provides a high
correlation coefficient (R=0.89).
Abstract: This study examined the effects of neuromuscular
training (NT) on limits of stability (LOS) in female individuals.
Twenty female basketball amateurs were assigned into NT
experimental group or control group by volunteer. All the players were
underwent regular basketball practice, 90 minutes, 3 times per week
for 6 weeks, but the NT experimental group underwent extra NT with
plyometric and core training, 50 minutes, 3 times per week for 6 weeks
during this period. Limits of stability (LOS) were evaluated by the
Biodex Balance System. One factor ANCOVA was used to examine
the differences between groups after training. The significant level for
statistic was set at p
Abstract: Detecting protein-protein interactions is a central problem in computational biology and aberrant such interactions may have implicated in a number of neurological disorders. As a result, the prediction of protein-protein interactions has recently received considerable attention from biologist around the globe. Computational tools that are capable of effectively identifying protein-protein interactions are much needed. In this paper, we propose a method to detect protein-protein interaction based on substring similarity measure. Two protein sequences may interact by the mean of the similarities of the substrings they contain. When applied on the currently available protein-protein interaction data for the yeast Saccharomyces cerevisiae, the proposed method delivered reasonable improvement over the existing ones.
Abstract: Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.
Abstract: The manufacture of large-scale precision aerospace
components using CNC requires a highly effective maintenance
strategy to ensure that the required accuracy can be achieved over
many hours of production. This paper reviews a strategy for a
maintenance management system based on Failure Mode Avoidance,
which uses advanced techniques and technologies to underpin a
predictive maintenance strategy. It is shown how condition
monitoring (CM) is important to predict potential failures in high
precision machining facilities and achieve intelligent and integrated
maintenance management. There are two distinct ways in which CM
can be applied. One is to monitor key process parameters and
observe trends which may indicate a gradual deterioration of
accuracy in the product. The other is the use of CM techniques to
monitor high status machine parameters enables trends to be
observed which can be corrected before machine failure and
downtime occurs.
It is concluded that the key to developing a flexible and intelligent
maintenance framework in any precision manufacturing operation is
the ability to evaluate reliably and routinely machine tool condition
using condition monitoring techniques within a framework of Failure
Mode Avoidance.
Abstract: To minimize power losses, it is important to
determine the location and size of local generators to be placed in
unbalanced power distribution systems. On account of some inherent
features of unbalanced distribution systems, such as radial structure,
large number of nodes, a wide range of X/R ratios, the conventional
techniques developed for the transmission systems generally fail on
the determination of optimum size and location of distributed
generators (DGs). This paper presents a simple method for
investigating the problem of contemporaneously choosing best
location and size of DG in three-phase unbalanced radial distribution
system (URDS) for power loss minimization and to improve the
voltage profile of the system. Best location of the DG is determined
by using voltage index analysis and size of DG is computed by
variational technique algorithm according to available standard size
of DGs. This paper presents the results of simulations for 25-bus and
IEEE 37- bus Unbalanced Radial Distribution system.
Abstract: A novel direction-of-arrival (DOA) estimation technique, which uses a conventional multiple signal classification (MUSIC) algorithm with periodic signals, is applied to a single RF-port parasitic array antenna for direction finding. Simulation results show that the proposed method gives high resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only one RF port and one analogue-to-digital converter (ADC) are used in this antenna, which features low DC power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling between elements. Therefore, the technique has great potential to be implemented into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals, to provide additional position location (PL) services.
Abstract: The association between emotional inhibition strategies linked to depression has been showed inconsistent among studies. Mild emotional inhibition maybe benefit for social interaction, especially for female among East Asian cultures. The present study aimed to examine whether the inhibition–depression relationship is dependent on level of emotion inhibition and gender context, given differing value of suppressing emotional displays. We hypothesized that the negative associations between inhibition and adolescent depression would not directly, in which affected by interaction between emotion inhibition and gender. To test this hypothesis, we asked 309 junior high school students which age range from 12 to14 years old to report on their use of emotion inhibition and depression syndrome. A multiple regressions analysis revealed that significant interaction that gender as a moderator to the relationships between emotion inhibition and adolescent depression. The group with the highest level of depression was girls with high levels of emotion inhibition, whose depression score was higher than that of boys with high levels of emotion inhibition. The result highlights that the importance of context in understanding the inhibition-depression relationship.
Abstract: This study is concerned with the investigation of the
suitability of several empirical and semi-empirical drying models
available in the literature to define drying behavior of viscose yarn
bobbins. For this purpose, firstly, experimental drying behaviour of
viscose bobbins was determined on an experimental dryer setup
which was designed and manufactured based on hot-air bobbin
dryers used in textile industry. Afterwards, drying models considered
were fitted to the experimentally obtained moisture ratios. Drying
parameters were drying temperature and bobbin diameter. The fit
was performed by selecting the values for constants in the models in
such a way that these values make the sum of the squared differences
between the experimental and the model results for moisture ratio
minimum. Suitability of fitting was specified as comparing the
correlation coefficient, standard error and mean square deviation.
The results show that the most appropriate model in describing the
drying curves of viscose bobbins is the Page model.
Abstract: In general, small-scale vegetables farmers experience
problems in improving the safety and quality of vegetables supplied
to high-class consumers in modern retailers. They also lack of
information to access market. The farmers group and/or cooperative
(FGC) should be able to assist its members by providing training in
handling and packing vegetables and enhancing marketing
capabilities to sell commodities to the modern retailers. This study
proposes an agri-food supply chain (ASC) model that involves the
corporate social responsibility (CSR) activities to cultivate the
capabilities of farmers to access market. Multi period ASC model is
formulated as Weighted Goal Programming (WGP) to analyze the
impacts of CSR programs to empower the FGCs in managing the
small-scale vegetables farmers. The results show that the proposed
model can be used to determine the priority of programs in order to
maximize the four goals to be achieved in the CSR programs.