Abstract: This study is concerned with pH solution detection
using 2 × 4 flexible sensor array based on a plastic polyethylene
terephthalate (PET) substrate that is coated a conductive layer and a
ruthenium dioxide (RuO2) sensitive membrane with the technologies
of screen-printing and RF sputtering. For data analysis, we also
prepared a dynamic measurement system for acquiring the response
voltage and analyzing the characteristics of the working electrodes
(WEs), such as sensitivity and linearity. In this condition, an array
measurement system was designed to acquire the original signal from
sensor array, and it is based on the method of digital signal processing
(DSP). The DSP modifies the unstable acquisition data to a direct
current (DC) output using the technique of digital filter. Hence, this
sensor array can obtain a satisfactory yield, 62.5%, through the design
measurement and analysis system in our laboratory.
Abstract: This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.
Abstract: Cytogenetic analysis still remains the gold standard method for prenatal diagnosis of trisomy 21 (Down syndrome, DS). Nevertheless, the conventional cytogenetic analysis needs live cultured cells and is too time-consuming for clinical application. In contrast, molecular methods such as FISH, QF-PCR, MLPA and quantitative Real-time PCR are rapid assays with results available in 24h. In the present study, we have successfully used a novel MGB TaqMan probe-based real time PCR assay for rapid diagnosis of trisomy 21 status in Down syndrome samples. We have also compared the results of this molecular method with corresponding results obtained by the cytogenetic analysis. Blood samples obtained from DS patients (n=25) and normal controls (n=20) were tested by quantitative Real-time PCR in parallel to standard G-banding analysis. Genomic DNA was extracted from peripheral blood lymphocytes. A high precision TaqMan probe quantitative Real-time PCR assay was developed to determine the gene dosage of DSCAM (target gene on 21q22.2) relative to PMP22 (reference gene on 17p11.2). The DSCAM/PMP22 ratio was calculated according to the formula; ratio=2 -ΔΔCT. The quantitative Real-time PCR was able to distinguish between trisomy 21 samples and normal controls with the gene ratios of 1.49±0.13 and 1.03±0.04 respectively (p value
Abstract: One of the main trouble in a steel strip manufacturing
line is the breakage of whatever weld carried out between steel coils,
that are used to produce the continuous strip to be processed. A weld
breakage results in a several hours stop of the manufacturing line. In
this process the damages caused by the breakage must be repaired.
After the reparation and in order to go on with the production it will
be necessary a restarting process of the line. For minimizing this
problem, a human operator must inspect visually and manually each
weld in order to avoid its breakage during the manufacturing process.
The work presented in this paper is based on the Bayesian decision
theory and it presents an approach to detect, on real-time, steel strip
defective welds. This approach is based on quantifying the tradeoffs
between various classification decisions using probability and the
costs that accompany such decisions.
Abstract: We apply a particle tracking technique to track the motion of individual pathogenic Leptospira. We observe and capture images of motile Leptospira by means of CCD and darkfield microscope. Image processing, statistical theories and simulations are used for data analysis. Based on trajectory patterns, mean square displacement, and power spectral density characteristics, we found that the motion modes are most likely to be directed motion mode (70%) and the rest are either normal diffusion or unidentified mode. Our findings may support the fact that why leptospires are very well efficient toward targeting internal tissues as a result of increase in virulence factor.
Abstract: Among various HLM techniques, the Multivariate Hierarchical Linear Model (MHLM) is desirable to use, particularly when multivariate criterion variables are collected and the covariance structure has information valuable for data analysis. In order to reflect prior information or to obtain stable results when the sample size and the number of groups are not sufficiently large, the Bayes method has often been employed in hierarchical data analysis. In these cases, although the Markov Chain Monte Carlo (MCMC) method is a rather powerful tool for parameter estimation, Procedures regarding MCMC have not been formulated for MHLM. For this reason, this research presents concrete procedures for parameter estimation through the use of the Gibbs samplers. Lastly, several future topics for the use of MCMC approach for HLM is discussed.
Abstract: The purpose of this study was to investigate the effectiveness of a recreational workout program for adults with disabilities over two semesters. This investigation was an action study conducted in a naturalistic setting. Participants included equal numbers of adults with severe cognitive impairments (n = 35) and adults without disabilities (n = 35). Adults with disabilities severe cognitive impairments were trained 6 self-initiated workout activities over two semesters by adults without disabilities. The numbers of task-analyzed steps of each activity performed correctly by each participant at the first and last weeks of each semester were used for data analysis. Results of the paired t-tests indicate that across two semesters, significant differences between the first and last weeks were found on 4 out of the 6 task-analyzed workout activities at a statistical level of significance p < .05. The recreational workout program developed in this study was effective.
Abstract: The purposes of the study are to study and to
investigate the relationship among exposure, uses and gratifications
of television morning news among undergraduate students in
Bangkok. This study also compares differences in information
exposure, uses and gratifications of television morning news among
these students.
The research methodology employed a questionnaire as a
quantitative method. The respondents were undergraduate students at
public and private universities in Bangkok. Totally, 400 usable
questionnaires were received. Descriptive and inferential statistics
were used in data analysis.
The results indicated that information exposure of undergraduate
students in Bangkok was at a high level. Students’ uses and
gratifications were also at high level. Information exposure was
positively correlated with uses and gratifications. Uses of information
were positively correlated with satisfaction with information. The
results also showed that students with differences in sex and type of
university were not significantly different in information exposure,
and uses and gratifications.
Abstract: The use and management of projects has risen to
a new prominence, with projects seen as critical to economic in
both the private and public sectors due challenging and dynamic
business environment. However, failure in managing project is
encountered regularly, which cause the waste of company
resources. The impacts of projects that failed to meet
stakeholders expectations have left behind long lasting negative
consequences in organization. Therefore, this research aims to
investigate on key success factors of project management in an
organization. It is believed that recognizing important factors
that contribute to successful project will help companies to
increase the overall profitability. 150 questionnaires were
distributed to respondents and 110 questionnaires were collected
and used in performing the data analysis. The result has strongly
supported the relationship between independent variables and
project performance.
Abstract: The Taiwan government has invested approximately
21 billion NT dollars in the construction of bicycle paths since
bicycling has gained huge popularity as a healthy leisure and
recreational activity. This study focuses on the behavior of
recreational bicyclists in Danshuei and Bali, northern Taiwan. Data
were collected from a field investigation carried out along the
Danshuei bicycle path and Bali left-bank bicycle path. A total of 578
questionnaires were gathered for data analysis. Descriptive statistics
and Chi-Square tests were used to assess bicyclists- behaviors. The
frequency shows that, in these areas, Danshuei and Bali, most
bicyclists rented bicycles, rode the bicycle path in the afternoon for
about 2 hours. The used the bicycle path one time per week. For most,
it was the first time to ride these bicycle paths. There were significant
differences in distribution of bicycle ownership, time of day, duration
of ride, ride frequency, and whether riding occurred on weekdays or
weekends. Results indicated that most bicyclists in Danshuei and Bali
were infrequent users.
Abstract: Intelligent traffic surveillance technology is an issue in
the field of traffic data analysis. Therefore, we need the technology to
detect moving objects in real-time while there are variations in background and natural light. In this paper, we proposed a Weighted-Center Surround Difference
method for object detection in outdoor environments. The proposed system detects objects using the saliency map that is obtained by
analyzing the weight of each layers of Gaussian pyramid. In order to validate the effectiveness of our system, we implemented the proposed
method using a digital signal processor, TMS320DM6437.
Experimental results show that blurred noisy around objects was effectively eliminated and the object detection accuracy is improved.
Abstract: Smoothing or filtering of data is first preprocessing step
for noise suppression in many applications involving data analysis.
Moving average is the most popular method of smoothing the data,
generalization of this led to the development of Savitzky-Golay filter.
Many window smoothing methods were developed by convolving
the data with different window functions for different applications;
most widely used window functions are Gaussian or Kaiser. Function
approximation of the data by polynomial regression or Fourier
expansion or wavelet expansion also gives a smoothed data. Wavelets
also smooth the data to great extent by thresholding the wavelet
coefficients. Almost all smoothing methods destroys the peaks and
flatten them when the support of the window is increased. In certain
applications it is desirable to retain peaks while smoothing the data
as much as possible. In this paper we present a methodology called
as peak-wise smoothing that will smooth the data to any desired level
without losing the major peak features.
Abstract: The purpose of this paper is to present two different
approaches of financial distress pre-warning models appropriate for
risk supervisors, investors and policy makers. We examine a sample
of the financial institutions and electronic companies of Taiwan
Security Exchange (TSE) market from 2002 through 2008. We
present a binary logistic regression with paned data analysis. With
the pooled binary logistic regression we build a model including
more variables in the regression than with random effects, while the
in-sample and out-sample forecasting performance is higher in
random effects estimation than in pooled regression. On the other
hand we estimate an Adaptive Neuro-Fuzzy Inference System
(ANFIS) with Gaussian and Generalized Bell (Gbell) functions and
we find that ANFIS outperforms significant Logit regressions in both
in-sample and out-of-sample periods, indicating that ANFIS is a
more appropriate tool for financial risk managers and for the
economic policy makers in central banks and national statistical
services.
Abstract: This paper proposes a modeling methodology for the
development of data analysis solution. The Author introduce the
approach to address data warehousing issues at the at enterprise level.
The methodology covers the process of the requirements eliciting and
analysis stage as well as initial design of data warehouse. The paper
reviews extended business process model, which satisfy the needs of
data warehouse development. The Author considers that the use of
business process models is necessary, as it reflects both enterprise
information systems and business functions, which are important for
data analysis. The Described approach divides development into
three steps with different detailed elaboration of models. The
Described approach gives possibility to gather requirements and
display them to business users in easy manner.
Abstract: The objectives of this research paper were to study the
influencing factors that contributed to the success of electronic
commerce (e-commerce) and to study the approach to enhance the
standard of e-commerce for small and medium enterprises (SME).
The research paper focused the study on only sole proprietorship
SMEs in Bangkok, Thailand. The factors contributed to the success
of SME included business management, learning in the organization,
business collaboration, and the quality of website. A quantitative and
qualitative mixed research methodology was used. In terms of
quantitative method, a questionnaire was used to collect data from
251 sole proprietorships. The System Equation Model (SEM) was
utilized as the tool for data analysis. In terms of qualitative method,
an in-depth interview, a dialogue with experts in the field of ecommerce
for SMEs, and content analysis were used.
By using the adjusted causal relationship structure model, it was
revealed that the factors affecting the success of e-commerce for
SMEs were found to be congruent with the empirical data. The
hypothesis testing indicated that business management influenced the
learning in the organization, the learning in the organization
influenced business collaboration and the quality of the website, and
these factors, in turn, influenced the success of SMEs. Moreover, the
approach to enhance the standard of SMEs revealed that the majority
of respondents wanted to enhance the standard of SMEs to a high
level in the category of safety of e-commerce system, basic structure
of e-commerce, development of staff potentials, assistance of budget
and tax reduction, and law improvement regarding the e-commerce
respectively.
Abstract: This paper suggests a rethinking of the existing
research about Genetically Modified (GM) food. Since the first batch
of GM food was commercialised in the UK market, GM food rapidly
received and lost media attention in the UK. Disagreement on GM
food policy between the US and the EU has also drawn scholarly
attention to this issue. Much research has been carried out intending to
understand people-s views about GM food and the shaping of these
views. This paper was based on the data collected in twenty-nine
semi-structured interviews, which were examined through Erving
Goffman-s idea of self-presentation in interactions to suggest that the
existing studies investigating “consumer attitudes" towards GM food
have only considered the “front stage" in the dramaturgic metaphor.
This paper suggests that the ways in which people choose to present
themselves when participating these studies should be taken into
account during the data analysis.
Abstract: The aims of this study were to compare the
differences of being good membership behavior among faculties and
staffs of Suan Sunandha Rajabhat University with different sex, age,
income, education, marital status, and working period, and
investigate the relationships between organizational commitment and
being good membership behavior. The research methodology
employed a questionnaire as a quantitative method. The respondents
were 305 faculties and staffs of Suan Sunandha Rajabhat University.
This research used Percentage, Mean, Standard Deviation, t-test,
One-Way ANOVA Analysis of Variance, and Pearson’s Product
Moment Correlation Coefficient in data analysis. The results showed
that organizational commitment among faculties and staffs of Suan
Sunandha Rajabhat University was at a high level. In addition,
differences in sex, age, income, education, marital status, and
working period revealed differences in being good membership
behavior. The results also indicated that organizational commitment
was significantly related to being good membership behavior.
Abstract: This paper presents the development of analysis tools
for Home Agriculture project. The tools are required for monitoring
the condition of greenhouse which involves two components:
measurement hardware and data analysis engine. Measurement
hardware is functioned to measure environment parameters such as
temperature, humidity, air quality, dust and etc while analysis tool is
used to analyse and interpret the integrated data against the condition
of weather, quality of health, irradiance, quality of soil and etc. The
current development of the tools is completed for off-line data
recorded technique. The data is saved in MMC and transferred via
ZigBee to Environment Data Manager (EDM) for data analysis.
EDM converts the raw data and plot three combination graphs. It has
been applied in monitoring three months data measurement for
irradiance, temperature and humidity of the greenhouse..
Abstract: Cancers could normally be marked by a number of
differentially expressed genes which show enormous potential as
biomarkers for a certain disease. Recent years, cancer classification
based on the investigation of gene expression profiles derived by
high-throughput microarrays has widely been used. The selection of
discriminative genes is, therefore, an essential preprocess step in
carcinogenesis studies. In this paper, we have proposed a novel gene
selector using information-theoretic measures for biological
discovery. This multivariate filter is a four-stage framework through
the analyses of feature relevance, feature interdependence, feature
redundancy-dependence and subset rankings, and having been
examined on the colon cancer data set. Our experimental result show
that the proposed method outperformed other information theorem
based filters in all aspect of classification errors and classification
performance.