Abstract: This study proposes and tests a rescapturing elements of perceived gain and loss that, by perceived value of medical tourism products, influencintention of potential customers. Data from 301 usable qwere tested against the research model using the structmodeling approach. The results indicated that perceivedkey predictor of customer intentions. As for benefimedical quality, service quality and enjoyment wcomponents that significantly influenced the perceptiRegarding sacrifice, the effects of perceived risk on pewere significant. The findings can provide insights intohow destination countries can make medical tourism a wfor themselves and international patients.KeywordsMedical tourism, perceived valueintention.
Abstract: Beginning from the creator of integro-differential
equations Volterra, many scientists have investigated these
equations. Classic method for solving integro-differential
equations is the quadratures method that is successfully applied up
today. Unlike these methods, Makroglou applied hybrid methods
that are modified and generalized in this paper and applied to the
numerical solution of Volterra integro-differential equations. The
way for defining the coefficients of the suggested method is also
given.
Abstract: The use of new technologies such internet (e-mail, chat
rooms) and cell phones has steeply increased in recent years.
Especially among children and young people, use of technological
tools and equipments is widespread. Although many teachers and
administrators now recognize the problem of school bullying, few are
aware that students are being harassed through electronic
communication. Referred to as electronic bullying, cyber bullying, or
online social cruelty, this phenomenon includes bullying through email,
instant messaging, in a chat room, on a website, or through
digital messages or images sent to a cell phone. Cyber bullying is
defined as causing deliberate/intentional harm to others using internet
or other digital technologies. It has a quantitative research design nd
uses relational survey as its method. The participants consisted of
300 secondary school students in the city of Konya, Turkey. 195
(64.8%) participants were female and 105 (35.2%) were male. 39
(13%) students were at grade 1, 187 (62.1%) were at grade 2 and 74
(24.6%) were at grade 3. The “Cyber Bullying Question List"
developed by Ar─▒cak (2009) was given to students. Following
questions about demographics, a functional definition of cyber
bullying was provided. In order to specify students- human values,
“Human Values Scale (HVS)" developed by Dilmaç (2007) for
secondary school students was administered. The scale consists of 42
items in six dimensions. Data analysis was conducted by the primary
investigator of the study using SPSS 14.00 statistical analysis
software. Descriptive statistics were calculated for the analysis of
students- cyber bullying behaviour and simple regression analysis was
conducted in order to test whether each value in the scale could
explain cyber bullying behaviour.
Abstract: In this work, the primary compressive strength
components of human femur trabecular bone are qualitatively
assessed using image processing and wavelet analysis. The Primary
Compressive (PC) component in planar radiographic femur trabecular
images (N=50) is delineated by semi-automatic image processing
procedure. Auto threshold binarization algorithm is employed to
recognize the presence of mineralization in the digitized images. The
qualitative parameters such as apparent mineralization and total area
associated with the PC region are derived for normal and abnormal
images.The two-dimensional discrete wavelet transforms are utilized
to obtain appropriate features that quantify texture changes in medical
images .The normal and abnormal samples of the human femur are
comprehensively analyzed using Harr wavelet.The six statistical
parameters such as mean, median, mode, standard deviation, mean
absolute deviation and median absolute deviation are derived at level
4 decomposition for both approximation and horizontal wavelet
coefficients. The correlation coefficient of various wavelet derived
parameters with normal and abnormal for both approximated and
horizontal coefficients are estimated. It is seen that in almost all cases
the abnormal show higher degree of correlation than normals. Further
the parameters derived from approximation coefficient show more
correlation than those derived from the horizontal coefficients. The
parameters mean and median computed at the output of level 4 Harr
wavelet channel was found to be a useful predictor to delineate the
normal and the abnormal groups.
Abstract: This paper proposes a new performance characterization for the test strategy intended for second order filters denominated Transient Analysis Method (TRAM). We evaluate the ability of the addressed test strategy for detecting deviation faults under simultaneous statistical fluctuation of the non-faulty parameters. For this purpose, we use Monte Carlo simulations and a fault model that considers as faulty only one component of the filter under test while the others components adopt random values (within their tolerance band) obtained from their statistical distributions. The new data reported here show (for the filters under study) the presence of hard-to-test components and relatively low fault coverage values for small deviation faults. These results suggest that the fault coverage value obtained using only nominal values for the non-faulty components (the traditional evaluation of TRAM) seem to be a poor predictor of the test performance.
Abstract: The aim of this paper is to rank the impact of Object
Oriented(OO) metrics in fault prediction modeling using Artificial
Neural Networks(ANNs). Past studies on empirical validation of
object oriented metrics as fault predictors using ANNs have focused
on the predictive quality of neural networks versus standard
statistical techniques. In this empirical study we turn our attention to
the capability of ANNs in ranking the impact of these explanatory
metrics on fault proneness. In ANNs data analysis approach, there is
no clear method of ranking the impact of individual metrics. Five
ANN based techniques are studied which rank object oriented
metrics in predicting fault proneness of classes. These techniques are
i) overall connection weights method ii) Garson-s method iii) The
partial derivatives methods iv) The Input Perturb method v) the
classical stepwise methods. We develop and evaluate different
prediction models based on the ranking of the metrics by the
individual techniques. The models based on overall connection
weights and partial derivatives methods have been found to be most
accurate.
Abstract: The purpose of this study was to examine to what
extend classroom management efficacy, marital status, gender, and
teaching experience predict burnout among primary school teachers.
Participants of this study were 523 (345 female, 178 male) teachers
who completed inventories. The results of multiple regression
analysis indicated that three dimensions of teacher burnout
(Emotional Exhaustion, Depersonalization, Personal
Accomplishment) were affected differently from four predictor
variables. Findings indicated that for the emotional exhaustion,
classroom management efficacy, marital status and teaching
experience; for depersonalization dimension, classroom management
efficacy and marital status and finally for the personal
accomplishment dimension, classroom management efficacy, gender,
and teaching experience were significant predictors.
Abstract: Quantitative Structure-Activity Relationship (QSAR)
approach for discovering novel more active Calanone derivative as
anti-leukemia compound has been conducted. There are 6
experimental activities of Calanone compounds against leukemia cell
L1210 that are used as material of the research. Calculation of
theoretical predictors (independent variables) was performed by
AM1 semiempirical method. The QSAR equation is determined by
Principle Component Regression (PCR) analysis, with Log IC50 as
dependent variable and the independent variables are atomic net
charges, dipole moment (μ), and coefficient partition of noctanol/
water (Log P). Three novel Calanone derivatives that
obtained by this research have higher activity against leukemia cell
L1210 than pure Calanone.
Abstract: This paper will first describe predictor controllers
when the proportional-integral-derivative (PID) controllers are
inactive for procedures that have large delay time (LDT) in transfer
stage. Therefore in those states, the predictor controllers are better
than the PID controllers, then compares three types of predictor
controllers. The value of these controller-s parameters are obtained
by trial and error method, so here an effort has been made to obtain
these parameters by Ziegler-Nichols method. Eventually in this paper
Ziegler-Nichols method has been described and finally, a PIP
controller has been designed for a thermal system, which circulates
hot air to keep the temperature of a chamber constant.
Abstract: This paper considers the development of a two-point
predictor-corrector block method for solving delay differential
equations. The formulae are represented in divided difference form
and the algorithm is implemented in variable stepsize variable order
technique. The block method produces two new values at a single
integration step. Numerical results are compared with existing
methods and it is evident that the block method performs very well.
Stability regions of the block method are also investigated.
Abstract: Olomouc is a unique and complex landmark with
widespread forestation and land use. This research work was
conducted to assess important and complex land use change
trajectories in Olomouc region. Multi-temporal satellite data from
1991, 2001 and 2013 were used to extract land use/cover types by
object oriented classification method. To achieve the objectives, three
different aspects were used: (1) Calculate the quantity of each
transition; (2) Allocate location based landscape pattern (3) Compare
land use/cover evaluation procedure. Land cover change trajectories
shows that 16.69% agriculture, 54.33% forest and 21.98% other areas
(settlement, pasture and water-body) were stable in all three decade.
Approximately 30% of the study area maintained as a same land cove
type from 1991 to 2013. Here broad scale of political and socioeconomic
factors was also affect the rate and direction of landscape
changes. Distance from the settlements was the most important
predictor of land cover change trajectories. This showed that most of
landscape trajectories were caused by socio-economic activities and
mainly led to virtuous change on the ecological environment.
Abstract: The stem cells have ability to differentiated
themselves through mitotic cell division and various range of
specialized cell types. Cellular differentiation is a way by which few
specialized cell develops into more specialized.This paper studies the
fundamental problem of computational schema for an artificial neural
network based on chemical, physical and biological variables of
state. By doing this type of study system could be model for a viable
propagation of various economically important stem cells
differentiation. This paper proposes various differentiation outcomes
of artificial neural network into variety of potential specialized cells
on implementing MATLAB version 2009. A feed-forward back
propagation kind of network was created to input vector (five input
elements) with single hidden layer and one output unit in output
layer. The efficiency of neural network was done by the assessment
of results achieved from this study with that of experimental data
input and chosen target data. The propose solution for the efficiency
of artificial neural network assessed by the comparatative analysis of
“Mean Square Error" at zero epochs. There are different variables of
data in order to test the targeted results.
Abstract: There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA-s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in [17]. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems.
Abstract: Meeting users- requirements is one of predictors of project success. There should be a match between the expectations of the users and the perception of key project personnel with respect to usability and functionality. The aim of this study is to make a comparison of key project personnel-s and potential users- (customer representatives) evaluations of the relative importance of usability and functionality factors in a software design project. Analytical Network Process (ANP) was used to analyze the relative importance of the factors. The results show that navigation and interaction are the most significant factors,andsatisfaction and efficiency are the least important factors for both groups. Further, it can be concluded that having similar orders and scores of usability and functionality factors for both groups shows that key project personnel have captured the expectations and requirements of potential users accurately.
Abstract: A proof of convergence of a new continuation algorithm for computing the Analytic SVD for a large sparse parameter– dependent matrix is given. The algorithm itself was developed and numerically tested in [5].
Abstract: The main purpose of this study was to determine the predictors of academic achievement of student Information and Communications Technologies (ICT) teachers with different learning styles. Participants were 148 student ICT teachers from Ankara University. Participants were asked to fill out a personal information sheet, the Turkish version of Kolb-s Learning Style Inventory, Weinstein-s Learning and Study Strategies Inventory, Schommer's Epistemological Beliefs Questionnaire, and Eysenck-s Personality Questionnaire. Stepwise regression analyses showed that the statistically significant predictors of the academic achievement of the accommodators were attitudes and high school GPAs; of the divergers was anxiety; of the convergers were gender, epistemological beliefs, and motivation; and of the assimilators were gender, personality, and test strategies. Implications for ICT teaching-learning processes and teacher education are discussed.
Abstract: Human immunodeficiency virus infection and
acquired immunodeficiency syndrome is a global pandemic with
cases reporting from virtually every country and continues to be a
common infection in developing country like India.
Microalbuminuria is a manifestation of human immunodeficiency
virus associated nephropathy. Therefore, microalbuminuria may be
an early marker of human immunodeficiency virus associated
nephropathy, and screening for its presence may be beneficial. A
strikingly high prevalence of microalbuminuria among human
immunodeficiency virus infected patients has been described in
various studies. Risk factors for clinically significant proteinuria
include African - American race, higher human immunodeficiency
virus ribonucleic acid level and lower CD4 lymphocyte count. The
cardiovascular risk factors of increased systolic blood pressure and
increase fasting blood sugar level are strongly associated with
microalbuminuria in human immunodeficiency virus patient. These
results suggest that microalbuminuria may be a sign of current
endothelial dysfunction and micro-vascular disease and there is
substantial risk of future cardiovascular disease events. Positive
contributing factors include early kidney disease such as human
immunodeficiency virus associated nephropathy, a marker of end
organ damage related to co morbidities of diabetes or hypertension,
or more diffuse endothelial cells dysfunction. Nevertheless after
adjustment for non human immunodeficiency virus factors, human
immunodeficiency virus itself is a major risk factor. The presence of
human immunodeficiency virus infection is independent risk to
develop microalbuminuria in human immunodeficiency virus patient.
Cardiovascular risk factors appeared to be stronger predictors of
microalbuminuria than markers of human immunodeficiency virus
severity person with human immunodeficiency virus infection and
microalbuminuria therefore appear to potentially bear the burden of
two separate damage related to known vascular end organ damage
related to know vascular risk factors, and human immunodeficiency
virus specific processes such as the direct viral infection of kidney
cells.The higher prevalence of microalbuminuria among the human
immunodeficiency virus infected could be harbinger of future
increased risks of both kidney and cardiovascular disease. Further
study defining the prognostic significance of microalbuminuria
among human immunodeficiency virus infected persons will be
essential. Microalbuminuria seems to be a predictor of cardiovascular
disease in diabetic and non diabetic subjects, hence it can also be
used for early detection of micro vascular disease in human
immunodeficiency virus positive patients, thus can help to diagnose
the disease at the earliest.
Abstract: One of the most important parts of a cement factory is
the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral
movement of air and materials, together with chemical reactions take
place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only
in exceptional case; however, a large number of the involved parameter were crossed out and an approximation model was
presented instead. This issue caused many problems for designing a
cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using
nonlinear identification technique on the Locally Linear Neuro-
Fuzzy (LLNF) model. For the first time, a simulator model as well as
a predictor one with a precise fifteen minute prediction horizon for a
cement rotary kiln is presented. These models are trained by
LOLIMOT algorithm which is an incremental tree-structure
algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these
models. The data collected from White Saveh Cement Company is used for modeling.
Abstract: The appearance management behavior of tanning by gay men is examined through the lens of Impression Formation. The study proposes that body image, self-esteem, and internalized homophobia are connected and affect the motives for engaging in sun, salon, and cosmetic tanning. Motives examined were: to look masculine, to look attractive to (potential) partners, to look attractive in general, to socialize, to meet a peer standard, and for personal satisfaction. Using regression analysis to examine data of 103 gay men who engage in at least one method of tanning, results reveal that components of body image and internalized homophobia–but not self-esteem–are linked to various motives and methods of tanning. These findings support and extend the literature of Impression Formation Theory and provide practitioners in the health and healthrelated fields new avenues to pursue when dealing with diseases related to tanning.
Abstract: Emerging adulthood, between the ages of 18 and 25, as a distinct developmental stage extending from adolescence to young adulthood. The proportions composing the five-factor model are neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. In the literature, there is any study which includes the relationship between emerging adults loneliness and personality traits. Therefore, the relationship between emerging adults loneliness and personality traits have to be investigated. This study examines the association between the Big Five personality traits, and loneliness among Turkish emerging adults. A total of 220 emerging adults completed the NEO Five Factor Inventory (NEO-FFI), and the The UCLA Loneliness Scale (UCLALS). Correlation analysis showed that three Big Five personality dimensions which are Neuroticism (positively), and Extraversion and Aggreableness (negatively) are moderately correlated with emerging adults loneliness. Regression analysis shows that Extraversion, Aggreableness and Neuroticism are the most important predictors of emerging adults loneliness. Results can be discussed in the context of emerging adulthood theory.