Abstract: Chronic diseases prevailed along with economic
growth as well as life style changed in recent years in Taiwan.
According to the governmental statistics, hypertension related disease
is the tenth of death causes with 1,816 died directly from hypertension
in 2010. There were more death causes amongst the top ten had been
proofed that having strong association with the hypertension, such as
heart diseases, cardiovascular diseases, and diabetes. Hypertension or
High blood pressure is one of the major indicators for chronic diseases,
and was generally perceived as the major causes of mortality. The
literature generally suggested that regular physical exercise was
helpful to prevent the occurrence or to ease the progress of a
hypertension. This paper reported the process and outcomes in
detailed of an improvement project of physical exercise intervention
specific for hypertension patients. Physical information were
measured before and after the project to obtain information such as
weight, waistline, cholesterol (HD & LD), blood examination, as well
as self-perceived health status. The intervention project involved a
six-week exercise program, of which contained three times a week, 30
minutes of tutored physical exercise intervention. The project had
achieved several gains in changing the subjects- behavior in terms of
many important biophysical indexes. Around 20% of the participants
had significantly improved their cholesterols, BMI, and changed
unhealthy behaviors. Results from the project were encouraging, and
would be good reference for other samples.
Abstract: To extract the important physiological factors related to
diabetes from an oral glucose tolerance test (OGTT) by mathematical
modeling, highly informative but convenient protocols are required.
Current models require a large number of samples and extended
period of testing, which is not practical for daily use. The purpose
of this study is to make model assessments possible even from a
reduced number of samples taken over a relatively short period.
For this purpose, test values were extrapolated using a support
vector machine. A good correlation was found between reference and
extrapolated values in evaluated 741 OGTTs. This result indicates
that a reduction in the number of clinical test is possible through a
computational approach.
Abstract: In this paper a class of analog algorithms based on the
concept of Cellular Neural Network (CNN) is applied in some
processing operations of some important medical images, namely
retina images, for detecting various symptoms connected with
diabetic retinopathy. Some specific processing tasks like
morphological operations, linear filtering and thresholding are
proposed, the corresponding template values are given and
simulations on real retina images are provided.
Abstract: Sensory nerves in the foot play an important part in the diagnosis of various neuropathydisorders, especially in diabetes mellitus.However, a detailed description of the anatomical distribution of the nerves is currently lacking. A computationalmodel of the afferent nerves inthe foot may bea useful tool for the study of diabetic neuropathy. In this study, we present the development of an anatomically-based model of various major sensory nerves of the sole and dorsal sidesof the foot. In addition, we presentan algorithm for generating synthetic somatosensory nerve networks in the big-toe region of a right foot model. The algorithm was based on a modified version of the Monte Carlo algorithm, with the capability of being able to vary the intra-epidermal nerve fiber density in differentregionsof the foot model. Preliminary results from the combinedmodel show the realistic anatomical structure of the major nerves as well as the smaller somatosensory nerves of the foot. The model may now be developed to investigate the functional outcomes of structural neuropathyindiabetic patients.
Abstract: Diabetes is one of the high prevalence diseases
worldwide with increased number of complications, with retinopathy
as one of the most common one. This paper describes how data
mining and case-based reasoning were integrated to predict
retinopathy prevalence among diabetes patients in Malaysia. The
knowledge base required was built after literature reviews and
interviews with medical experts. A total of 140 diabetes patients- data
were used to train the prediction system. A voting mechanism selects
the best prediction results from the two techniques used. It has been
successfully proven that both data mining and case-based reasoning
can be used for retinopathy prediction with an improved accuracy of
85%.
Abstract: Hand gesture is an active area of research in the vision
community, mainly for the purpose of sign language recognition and
Human Computer Interaction. In this paper, we propose a system to
recognize alphabet characters (A-Z) and numbers (0-9) in real-time
from stereo color image sequences using Hidden Markov Models
(HMMs). Our system is based on three main stages; automatic segmentation
and preprocessing of the hand regions, feature extraction
and classification. In automatic segmentation and preprocessing stage,
color and 3D depth map are used to detect hands where the hand
trajectory will take place in further step using Mean-shift algorithm
and Kalman filter. In the feature extraction stage, 3D combined features
of location, orientation and velocity with respected to Cartesian
systems are used. And then, k-means clustering is employed for
HMMs codeword. The final stage so-called classification, Baum-
Welch algorithm is used to do a full train for HMMs parameters.
The gesture of alphabets and numbers is recognized using Left-Right
Banded model in conjunction with Viterbi algorithm. Experimental
results demonstrate that, our system can successfully recognize hand
gestures with 98.33% recognition rate.
Abstract: The excessive consumption of fossil energies (electrical energy) during summer caused by the technological development involves more and more climate warming.
In order to reduce the worst impact of gas emissions produced from classical air conditioning, heat driven solar absorption chiller is pretty promising; it consists on using solar as motive energy which is clean and environmentally friendly to provide cold.
Solar absorption machine is composed by four components using Lithium Bromide /water as a refrigerating couple. LiBr- water is the most promising in chiller applications due to high safety, high volatility ratio, high affinity, high stability and its high latent heat. The lithium bromide solution is constitute by the salt lithium bromide which absorbs water under certain conditions of pressure and temperature however if the concentration of the solution is high in the absorption chillers; which exceed 70%, the solution will crystallize.
The main aim of this article is to study the phenomena of the crystallization and to evaluate how the dependence between the electric conductivity and the concentration which should be controlled.
Abstract: Stevia rebaudiana Bertoni (natural sweetener) belongs
to Asteraceae family and can be used as substitute of artificial
sweeteners for diabetic patients. Conventionally, it is cultivated by
seeds or stem cutting, but seed viability rate is poor. A protocol for
callus induction and multiplication was developed to produce large
no. of calli in short period. Surface sterilized nodal, leaf and root
explants were cultured on Murashige and Skoog (MS) medium with
different concentrations of plant hormone like, IBA, kinetin, NAA,
2,4-D, and NAA in combination with 2,4-D. 100% callusing was
observed from leaf explants cultured on combination of NAA and
2,4-D after three weeks while with 2,4-D, only 10% callusing was
observed. Calli obtained from leaf and root explants were shiny green
while with nodal explants it was hard and brown. The present
findings deal with induction of callusing in Stevia to achieve the
rapid callus multiplication for study of steviol glycosides in callus
culture.
Abstract: On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.
Abstract: A cross sectional survey design was used to collect
data from 370 diabetic patients. Two instruments were used in
obtaining data; in-depth interview guide and researchers- developed
questionnaire. Fisher's exact test was used to investigate association
between the identified factors and nonadherence. Factors identified
were: socio-demographic factors such as: gender, age, marital status,
educational level and occupation; psychosocial obstacles such as:
non-affordability of prescribed diet, frustration due to the restriction,
limited spousal support, feelings of deprivation, feeling that
temptation is inevitable, difficulty in adhering in social gatherings
and difficulty in revealing to host that one is diabetic; health care
providers obstacles were: poor attitude of health workers, irregular
diabetes education in clinics , limited number of nutrition education
sessions/ inability of the patients to estimate the desired quantity of
food, no reminder post cards or phone calls about upcoming patient
appointments and delayed start of appointment / time wasting in
clinics.
Abstract: The spiral angle of the elementary cellulose fibril in
the wood cell wall, often called microfibril angle, (MFA). Microfibril
angle in hardwood is one of the key determinants of solid timber
performance due to its strong influence on the stiffness, strength,
shrinkage, swelling, thermal-dynamics mechanical properties and
dimensional stability of wood. Variation of MFA (degree) in the S2
layer of the cell walls among Acacia mangium trees was determined
using small-angle X-ray scattering (SAXS). The length and
orientation of the microfibrils of the cell walls in the irradiated
volume of the thin samples are measured using SAXS and optical
microscope for 3D surface measurement. The undetermined
parameters in the analysis are the MFA, (M) and the standard
deviation (σФ) of the intensity distribution arising from the wandering
of the fibril orientation about the mean value. Nine separate pairs of
values are determined for nine different values of the angle of the
incidence of the X-ray beam relative to the normal to the radial
direction in the sample. The results show good agreement. The
curve distribution of scattered intensity for the real cell wall structure
is compared with that calculated with that assembly of rectangular
cells with the same ratio of transverse to radial cell wall length. It is
demonstrated that for β = 45°, the peaks in the curve intensity
distribution for the real and the rectangular cells coincide. If this
peak position is Ф45, then the MFA can be determined from the
relation M = tan-1 (tan Ф45 / cos 45°), which is precise for rectangular
cells. It was found that 92.93% of the variation of MFA can be
attributed to the distance from pith to bark. Here we shall present our
results of the MFA in the cell wall with respect to its shape, structure
and the distance from pith to park as an important fast check and yet
accurate towards the quality of wood, its uses and application.
Abstract: In this paper, Tobephobia (TBP) alludes to the fear of
failure experienced by teachers to manage curriculum change. TBP is
an emerging concept and it extends the boundaries of research in
terms of how we view achievement and failure in education.
Outcomes-based education (OBE) was introduced fifteen years ago
in South African schools without simultaneously upgrading teachers-
professional competencies. This exploratory research, therefore
examines a simple question: What is the impact of TBP and OBE on
teachers? Teacher ineptitude to cope with the OBE curriculum in the
classroom is a serious problem affecting large numbers of South
African teachers. This exploratory study sought to determine the
perceived negative impact of OBE and TBP on teachers. A survey
was conducted amongst 311 teachers in Port Elizabeth and Durban,
South Africa. The results confirm the very negative impact of TBP
and OBE on teachers. This exploratory study authenticates the
existence of TBP.
Abstract: In this research, the diabetes conditions of people (healthy, prediabete and diabete) were tried to be identified with noninvasive palm perspiration measurements. Data clusters gathered from 200 subjects were used (1.Individual Attributes Cluster and 2. Palm Perspiration Attributes Cluster). To decrase the dimensions of these data clusters, Principal Component Analysis Method was used. Data clusters, prepared in that way, were classified with Support Vector Machines. Classifications with highest success were 82% for Glucose parameters and 84% for HbA1c parametres.
Abstract: The objective of the present communication is to
develop new genuine exponentiated mean codeword lengths and to
study deeply the problem of correspondence between well known
measures of entropy and mean codeword lengths. With the help of
some standard measures of entropy, we have illustrated such a
correspondence. In literature, we usually come across many
inequalities which are frequently used in information theory.
Keeping this idea in mind, we have developed such inequalities via
coding theory approach.
Abstract: Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.