Abstract: This paper present the study carried out of accident
analysis, black spot study and to develop accident predictive models
based on the data collected at rural roadway, Federal Route 50 (F050)
Malaysia. The road accident trends and black spot ranking were
established on the F050. The development of the accident prediction
model will concentrate in Parit Raja area from KM 19 to KM 23.
Multiple non-linear regression method was used to relate the discrete
accident data with the road and traffic flow explanatory variable. The
dependent variable was modeled as the number of crashes namely
accident point weighting, however accident point weighting have
rarely been account in the road accident prediction Models. The result
show that, the existing number of major access points, without traffic
light, rise in speed, increasing number of Annual Average Daily
Traffic (AADT), growing number of motorcycle and motorcar and
reducing the time gap are the potential contributors of increment
accident rates on multiple rural roadway.
Abstract: The purposes of this research are 1) to study English language learning strategies used by the fourth-year students majoring in English and Business English, 2) to study the English language learning strategies which have an affect on English learning achievement, and 3) to compare the English language learning strategies used by the students majoring in English and Business English. The population and sampling comprise of 139 university students of the Suan Sunandha Rajabhat University. Research instruments are language learning strategies questionnaire which was constructed by the researcher and improved on by three experts and the transcripts that show the results of English learning achievement. The questionnaire includes 1) Language Practice Strategy 2)Memory Strategy 3) Communication Strategy 4)Making an Intelligent Guess or Compensation Strategy 5) Self-discipline in Learning Management Strategy 6) Affective Strategy 7)Self-Monitoring Strategy 8) Self-studySkill Strategy. Statistics used in the study are mean, standard deviation, T-test and One Way ANOVA, Pearson product moment correlation coefficient and Regression Analysis. The results of the findings reveal that the English language learning strategies most frequently used by the students are affective strategy, making an intelligent guess or compensation strategy, self-studyskill strategy and self-monitoring strategy respectively. The aspect of making an intelligent guess or compensation strategy had the most significant affect on English learning achievement. It is found that the English language learning strategies mostly used by the Business English major students and moderately used by the English major students. Their language practice strategies uses were significantly different at the 0.05 level and their communication strategies uses were significantly different at the 0.01 level. In addition, it is found that the poor students and the fair ones most frequently used affective strategy while the good ones most frequently used making an intelligent guess or compensation strategy. KeywordsEnglish language, language learning strategies, English learning achievement, and students majoring in English, Business English. Pranee Pathomchaiwat is an Assistant Professor in Business English Program, Suan Sunandha Rajabhat University, Bangkok, Thailand (e-mail: [email protected]).
Abstract: In this paper, the sum of squares in linear regression is
reduced to sum of squares in semi-parametric regression. We
indicated that different sums of squares in the linear regression are
similar to various deviance statements in semi-parametric regression.
In addition to, coefficient of the determination derived in linear
regression model is easily generalized to coefficient of the
determination of the semi-parametric regression model. Then, it is
made an application in order to support the theory of the linear
regression and semi-parametric regression. In this way, study is
supported with a simulated data example.
Abstract: Internet access is a vital part of the modern world and an important tool in the education of our children. It is present in schools, homes and even shopping malls. Mastering the use of the internet is likely to be an important skill for those entering the job markets of the future. An internet user can be anyone he or she wants to be in an online chat room, or play thrilling and challenging games against other players from all corners of the globe. It seems at present time (or near future) for many people relationships in the real world may be neglected as those in the virtual world increase in importance. Internet is provided a fast mode of transportation caused freedom from family bonds and mixing with different cultures and new communities. This research is an attempt to study effect of Internet on Social capital. For this purpose a survey technique on the sample size amounted 168 students of Payame Noor University of Kermanshah city in country of Iran were considered. Degree of social capital is moderate. With the help of the Multi-variable Regression, variables of Iranian message attractive, Interest to internet with effect of positive and variable Creating a cordial atmosphere with negative effect be significant.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: Team efficacy beliefs show promise in enhancing
team performance. Using a model-based quantitative research design,
we investigated the antecedents and performance consequences of
generalized team efficacy (potency) in a sample of 56 capital projects
executed by 15 Fortune 500 companies in the process industries.
Empirical analysis of our field survey identified that generalized
team efficacy beliefs were positively associated with an objective
measure of project cost performance. Regression analysis revealed
that team competence, empowering leadership, and performance
feedback all predicted generalized team efficacy beliefs. Tests of
mediation revealed that generalized team efficacy fully mediated
between these three inputs and project cost performance.
Abstract: This study describes the methodology for the development of a validated in-vitro in-vivo correlation (IVIVC) for metoprolol tartrate modified release dosage forms with distinctive release rate characteristics. Modified release dosage forms were formulated by microencapsulation of metoprolol tartrate into different amounts of ethylcellulose by non-solvent addition technique. Then in-vitro and in-vivo studies were conducted to develop and validate level A IVIVC for metoprolol tartrate. The values of regression co-efficient (R2-values) for IVIVC of T2 and T3 formulations were not significantly (p
Abstract: The main thrust of this paper is to assess the level of disclosure in the annual reports of non-financial Greek firms and to empirically investigate the hypothesized impact of several firm characteristics on the extent of mandatory disclosure. A disclosure checklist consisting of 100 mandatory items was developed to assess the level of disclosure in the 2009 annual reports of 43 Greek companies listed at the Athens stock exchange. The association between the level of disclosure and some firm characteristics was examined using multiple linear regression analysis. The study reveals that Greek companies on general have responded adequately to the mandatory disclosure requirements of the regulatory bodies. The findings also indicate that firm size was significant positively associated with the level of disclosure. The remaining variables such as age, profitability, liquidity, and board composition were found to be insignificant in explaining the variation of mandatory disclosures. The outcome of this study is undoubtedly of great concern to the investment community at large to assist in evaluating the extent of mandatory disclosure by Greek firms and explaining the variation of disclosure in light of firm-specific characteristics.
Abstract: Water samples were collected from river Pandu at six
stations where human and animal activities were high. Composite
samples were analyzed for dissolved oxygen (DO), biochemical
oxygen demand (BOD), chemical oxygen demand (COD) , pH values
during dry and wet seasons as well as the harmattan period. The total
data points were used to establish relationships between the
parameters and data were also subjected to statistical analysis and
expressed as mean ± standard error of mean (SEM) at a level of
significance of p
Abstract: Accurate demand forecasting is one of the most key
issues in inventory management of spare parts. The problem of
modeling future consumption becomes especially difficult for lumpy
patterns, which characterized by intervals in which there is no
demand and, periods with actual demand occurrences with large
variation in demand levels. However, many of the forecasting
methods may perform poorly when demand for an item is lumpy.
In this study based on the characteristic of lumpy demand patterns
of spare parts a hybrid forecasting approach has been developed,
which use a multi-layered perceptron neural network and a
traditional recursive method for forecasting future demands. In the
described approach the multi-layered perceptron are adapted to
forecast occurrences of non-zero demands, and then a conventional
recursive method is used to estimate the quantity of non-zero
demands. In order to evaluate the performance of the proposed
approach, their forecasts were compared to those obtained by using
Syntetos & Boylan approximation, recently employed multi-layered
perceptron neural network, generalized regression neural network
and elman recurrent neural network in this area. The models were
applied to forecast future demand of spare parts of Arak
Petrochemical Company in Iran, using 30 types of real data sets. The
results indicate that the forecasts obtained by using our proposed
mode are superior to those obtained by using other methods.
Abstract: We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.
Abstract: In this article, models based on quantitative analysis,
physical geometry and regression analysis are established, by using
analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy
photographic and data fitting. The reasons of various leaf shapes
among different species and the differences between the leaf shapes on
same tree have been solved by using software, such as Eviews, VB and
Matlab. We also successfully estimate the leaf mass of a tree and the
correlation with the tree profile.
Abstract: Com Poisson distribution is capable of modeling the count responses irrespective of their mean variance relation and the parameters of this distribution when fitted to a simple cross sectional data can be efficiently estimated using maximum likelihood (ML) method. In the regression setup, however, ML estimation of the parameters of the Com Poisson based generalized linear model is computationally intensive. In this paper, we propose to use quasilikelihood (QL) approach to estimate the effect of the covariates on the Com Poisson counts and investigate the performance of this method with respect to the ML method. QL estimates are consistent and almost as efficient as ML estimates. The simulation studies show that the efficiency loss in the estimation of all the parameters using QL approach as compared to ML approach is quite negligible, whereas QL approach is lesser involving than ML approach.
Abstract: This article illustrates that how non similar culture become a cause of constant anxiety among international students in China. For that, a survey was carried out among international students of Wuhan University, China. The association among non similar culture, non familiarity of Chinese culture, self finance students and food problem is looked at through a regression line, and in the light of empirical results, a model is anticipated which elucidates these results. Some suggestions were directed at the end which will help to mitigate the anxiety among prospective students in Chinese universities.
Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.
Abstract: This study analyzes characteristics determining
member’s willingness to invest in cooperatives using ordered logit
model. The data were collected in a field survey among 122
cooperative members in north-central China. The descriptive analysis
of survey evidence suggests that cooperatives in China generally
having poor ability to deliver the processing services related to
product package, grading, and storage, performing worse in
profitability, inability of providing returns to capital and obtaining
agricultural loan. The regression results demonstrate that members’
farm size, their satisfaction with cooperative price preferential
services, attitudes toward cooperative operational scale and
development potential have statistically significant impact on
willingness to invest.
Abstract: Cluster analysis divides data into groups that are
meaningful, useful, or both. Analysis of biological data is creating a
new generation of epidemiologic, prognostic, diagnostic and
treatment modalities. Clustering of protein sequences is one of the
current research topics in the field of computer science. Linear
relation is valuable in rule discovery for a given data, such as if value
X goes up 1, value Y will go down 3", etc. The classical linear
regression models the linear relation of two sequences perfectly.
However, if we need to cluster a large repository of protein sequences
into groups where sequences have strong linear relationship with
each other, it is prohibitively expensive to compare sequences one by
one. In this paper, we propose a new technique named General
Regression Model Technique Clustering Algorithm (GRMTCA) to
benignly handle the problem of linear sequences clustering. GRMT
gives a measure, GR*, to tell the degree of linearity of multiple
sequences without having to compare each pair of them.
Abstract: This study discusses the stumbling blocks stifling the
adoption of GPS technology in the public sector of Pakistan. This
study has been carried out in order to describe the value of GPS
technology and its adoption at various public sector organisations in
Pakistan. Sample size for the research conducted was 200; personnel
working in public sector having age above 29 years were surveyed.
Data collected for this research has been quantitatively analysed with
the help of SPSS. Regression analysis, correlation and cross
tabulation were the techniques used to determine the strength of
relationship between key variables. Findings of this research indicate
that main hurdles in GPS adoption in the public sector of Pakistan are
lack of awareness about GPS among masses in general and the
stakeholders in particular, lack of initiative on part of government in
promoting new technologies, unavailability of GPS infrastructure in
Pakistan and prohibitions on map availability because of security
reasons.
Abstract: The Emergency Department of a medical center in
Taiwan cooperated to conduct the research. A predictive model of
triage system is contracted from the contract procedure, selection of
parameters to sample screening. 2,000 pieces of data needed for the
patients is chosen randomly by the computer. After three
categorizations of data mining (Multi-group Discriminant Analysis,
Multinomial Logistic Regression, Back-propagation Neural
Networks), it is found that Back-propagation Neural Networks can
best distinguish the patients- extent of emergency, and the accuracy
rate can reach to as high as 95.1%. The Back-propagation Neural
Networks that has the highest accuracy rate is simulated into the triage
acuity expert system in this research. Data mining applied to the
predictive model of the triage acuity expert system can be updated
regularly for both the improvement of the system and for education
training, and will not be affected by subjective factors.
Abstract: Concerning the measurement of friction properties of
textiles and fabrics using Kawabata Evaluation System (KES), whose
output is constrained to the surface friction factor of fabric, and no
other data would be generated; this research has been conducted to
gain information about surface roughness regarding its surface
friction factor. To assess roughness properties of light nonwovens, a
3-dimensional model of a surface has been simulated with regular
sinuous waves through it as an ideal surface. A new factor was
defined, namely Surface Roughness Factor, through comparing
roughness properties of simulated surface and real specimens. The
relation between the proposed factor and friction factor of specimens
has been analyzed by regression, and results showed a meaningful
correlation between them. It can be inferred that the new presented
factor can be used as an acceptable criterion for evaluating the
roughness properties of light nonwoven fabrics.