Abstract: Analysis of blood vessel mechanics in normal and
diseased conditions is essential for disease research, medical device
design and treatment planning. In this work, 3D finite element
models of normal vessel and atherosclerotic vessel with 50% plaque
deposition were developed. The developed models were meshed
using finite number of tetrahedral elements. The developed models
were simulated using actual blood pressure signals. Based on the
transient analysis performed on the developed models, the parameters
such as total displacement, strain energy density and entropy per unit
volume were obtained. Further, the obtained parameters were used to
develop artificial neural network models for analyzing normal and
atherosclerotic blood vessels. In this paper, the objectives of the
study, methodology and significant observations are presented.
Abstract: The incidence of oral cancer in Taiwan increased year
by year. It replaced the nasopharyngeal as the top incurrence among
head and neck cancers since 1994. Early examination and earlier
identification for earlier treatment is the most effective medical
treatment for these cancers. Although the government fully subsidized
the expenses with tremendous promotion program for oral cancer
screening, the citizen-s participation remained low. Purpose of this
study is to understand the factors affecting the citizens- behavior
intensions of taking an oral cancer screening. Based on the Theory of
Planned Behavior, this study adopted four distinctive variables in
explaining the captioned behavior intentions.700 questionnaires were
dispatched with 500 valid responses or 71.4% returned by the citizens
with an age 30 or above from the eastern counties of Taiwan. Test
results has shown that attitude toward, subjective norms of, and
perceived behavioral control over the oral cancer screening varied
from some demographic factors to another. The study proofed that
attitude toward, subjective norms of, and perceived behavioral control
over the oral cancer screening had positive impacts on the
corresponding behavior intention. The test concluded that the theory
of planned behavior was appropriate as a theoretical framework in
explaining the influencing factors of intentions of taking oral cancer
screening. This study suggested the healthcare professional should
provide high accessibility of screening services other than just
delivering knowledge on oral cancer to promote the citizens-
intentions of taking the captioned screening. This research also
provided a practical implication to the healthcare professionals when
formulating and implementing promotion instruments for lifting the
screening rate of oral cancer.
Abstract: In this study, The physico-chemical and nutritional
properties of `Musmula` Medlar (Mespilus germanica L.) fruit and
seed grown in Northeast Anatolia was investigated. In the fruit,
length, width, thickness, weight, total soluble solids, colour (1),
colour (2) [L, a, b values], protein, crude ash, crude fiber, crude oil,
texture and pH were determinated as 4.34 cm, 4.22 cm, 3.67 cm,
38.36 g, 23.97 %, S60O60Y41,, [53.85, 17.15, 33.75], 1.06 %, 0.79 %,
4.24 %, 0.005 %, 1.21 kg/cm2 and 4.26 respectively. Also, pulp ratio,
seed ratio and pulp/seed ratio were found to be 92.88 %, 7.11 % and
14.07 %, respectively. In addition, the mineral composition of medlar
fruit in Northeast Anatolia was studied. In the fruit, 23 minerals were
analyzed and 19 minerals were present at detectable levels. The
medlar fruit was richest in potassium (6962 ppm), calcium (1186.378
ppm), magnesium (1070.08 ppm) and phosphor (763.425 ppm).
Abstract: Recently, nanomaterials are developed in the form of nano-films, nano-crystals and nano-pores. Lanthanide phosphates as a material find extensive application as laser, ceramic, sensor, phosphor, and also in optoelectronics, medical and biological labels, solar cells and light sources. Among the different kinds of rare-earth orthophosphates, yttrium orthophosphate has been shown to be an efficient host lattice for rare earth activator ions, which have become a research focus because of their important role in the field of light display systems, lasers, and optoelectronic devices. It is in this context that the 4fn- « 4fn-1 5d transitions of rare earth in insulating materials, lying in the UV and VUV, are the aim of large number of studies .Though there has been a few reports on Eu3+, Nd3+, Pr3+,Er3+, Ce3+, Tm3+ doped YPO4. The 4fn- « 4fn-1 5d transitions of the rare earth dependent to the host-matrix, several matrices ions were used to study these transitions, in this work we are suggesting to study on a very specific class of inorganic material that are orthophosphate doped with rare earth ions. This study focused on the effect of Ce3+ concentration on the structural and optical properties of Ce3+ doped YPO4 yttrium orthophosphate with powder form prepared by the Sol Gel method.
Abstract: In the semiconductor manufacturing process, large
amounts of data are collected from various sensors of multiple
facilities. The collected data from sensors have several different characteristics
due to variables such as types of products, former processes
and recipes. In general, Statistical Quality Control (SQC) methods
assume the normality of the data to detect out-of-control states of
processes. Although the collected data have different characteristics,
using the data as inputs of SQC will increase variations of data,
require wide control limits, and decrease performance to detect outof-
control. Therefore, it is necessary to separate similar data groups
from mixed data for more accurate process control. In the paper,
we propose a regression tree using split algorithm based on Pearson
distribution to handle non-normal distribution in parametric method.
The regression tree finds similar properties of data from different
variables. The experiments using real semiconductor manufacturing
process data show improved performance in fault detecting ability.
Abstract: Uranium mining and processing in Brazil occur in a
northeastern area near to Caetité-BA. Several Non-Governmental
Organizations claim that uranium mining in this region is a pollutant
causing health risks to the local population,but those in charge of the
complex extraction and production of“yellow cake" for generating
fuel to the nuclear power plants reject these allegations. This study
aimed at identifying potential problems caused by mining to the
population of Caetité. In this, work,the concentrations of 238U, 232Th
and 40K radioisotopes in the teeth of the Caetité population were
determined by ICP-MS. Teeth are used as bioindicators of
incorporated radionuclides. Cumulative radiation doses in the
skeleton were also determined. The concentration values were below
0.008 ppm, and annual effective dose due to radioisotopes are below
to the reference values. Therefore, it is not possible to state that the
mining process in Caetité increases pollution or radiation exposure in
a meaningful way.
Abstract: If organizations like Mellat Bank want to identify its
customer market completely to reach its specified goals, it can
segment the market to offer the product package to the right segment.
Our objective is to offer a segmentation model for Iran banking
market in Mellat bank view. The methodology of this project is
combined by “segmentation on the basis of four part-quality
variables" and “segmentation on the basis of different in means".
Required data are gathered from E-Systems and researcher personal
observation. Finally, the research offers the organization that at first
step form a four dimensional matrix with 756 segments using four
variables named value-based, behavioral, activity style, and activity
level, and at the second step calculate the means of profit for every
cell of matrix in two distinguished work level (levels α1:normal
condition and α2: high pressure condition) and compare the segments
by checking two conditions that are 1- homogeneity every segment
with its sub segment and 2- heterogeneity with other segments, and
so it can do the necessary segmentation process. After all, the last
offer (more explained by an operational example and feedback
algorithm) is to test and update the model because of dynamic
environment, technology, and banking system.
Abstract: Unified Speech Audio Coding (USAC), the latest MPEG standardization for unified speech and audio coding, uses a speech/audio classification algorithm to distinguish speech and audio segments of the input signal. The quality of the recovered audio can be increased by well-designed orchestra/percussion classification and subsequent processing. However, owing to the shortcoming of the system, introducing an orchestra/percussion classification and modifying subsequent processing can enormously increase the quality of the recovered audio. This paper proposes an orchestra/percussion classification algorithm for the USAC system which only extracts 3 scales of Mel-Frequency Cepstral Coefficients (MFCCs) rather than traditional 13 scales of MFCCs and use Iterative Dichotomiser 3 (ID3) Decision Tree rather than other complex learning method, thus the proposed algorithm has lower computing complexity than most existing algorithms. Considering that frequent changing of attributes may lead to quality loss of the recovered audio signal, this paper also design a modified subsequent process to help the whole classification system reach an accurate rate as high as 97% which is comparable to classical 99%.
Abstract: A new genetic algorithm, termed the 'optimum individual monogenetic genetic algorithm' (OIMGA), is presented whose properties have been deliberately designed to be well suited to hardware implementation. Specific design criteria were to ensure fast access to the individuals in the population, to keep the required silicon area for hardware implementation to a minimum and to incorporate flexibility in the structure for the targeting of a range of applications. The first two criteria are met by retaining only the current optimum individual, thereby guaranteeing a small memory requirement that can easily be stored in fast on-chip memory. Also, OIMGA can be easily reconfigured to allow the investigation of problems that normally warrant either large GA populations or individuals many genes in length. Local convergence is achieved in OIMGA by retaining elite individuals, while population diversity is ensured by continually searching for the best individuals in fresh regions of the search space. The results given in this paper demonstrate that both the performance of OIMGA and its convergence time are superior to those of a range of existing hardware GA implementations.
Abstract: Diffuse viral encephalitis may lack fever and other cardinal signs of infection and hence its distinction from other acute encephalopathic illnesses is challenging. Often, the EEG changes seen routinely are nonspecific and reflect diffuse encephalopathic changes only. The aim of this study was to use nonlinear dynamic mathematical techniques for analyzing the EEG data in order to look for any characteristic diagnostic patterns in diffuse forms of encephalitis.It was diagnosed on clinical, imaging and cerebrospinal fluid criteria in three young male patients. Metabolic and toxic encephalopathies were ruled out through appropriate investigations. Digital EEGs were done on the 3rd to 5th day of onset. The digital EEGs of 5 male and 5 female age and sex matched healthy volunteers served as controls.Two sample t-test indicated that there was no statistically significant difference between the average values in amplitude between the two groups. However, the standard deviation (or variance) of the EEG signals at FP1-F7 and FP2-F8 are significantly higher for the patients than the normal subjects. The regularisation dimension is significantly less for the patients (average between 1.24-1.43) when compared to the normal persons (average between 1.41-1.63) for the EEG signals from all locations except for the Fz-Cz signal. Similarly the wavelet dimension is significantly less (P = 0.05*) for the patients (1.122) when compared to the normal person (1.458). EEGs are subdued in the case of the patients with presence of uniform patterns, manifested in the values of regularisation and wavelet dimensions, when compared to the normal person, indicating a decrease in chaotic nature.
Abstract: This paper presents an application of 5S lean technology to a production facility. Due to increased demand, high product variety, and a push production system, the plant has suffered from excessive wastes, unorganized workstations, and unhealthy work environment. This has translated into increased production cost, frequent delays, and low workers morale. Under such conditions, it has become difficult, if not impossible, to implement effective continuous improvement studies. Hence, the lean project is aimed at diagnosing the production process, streamlining the workflow, removing/reducing process waste, cleaning the production environment, improving plant layout, and organizing workstations. 5S lean technology is utilized for achieving project objectives. The work was a combination of both culture changes and tangible/physical changes on the shop floor. The project has drastically changed the plant and developed the infrastructure for a successful implementation of continuous improvement as well as other best practices and quality initiatives.
Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: The clustering ensembles combine multiple partitions
generated by different clustering algorithms into a single clustering
solution. Clustering ensembles have emerged as a prominent method
for improving robustness, stability and accuracy of unsupervised
classification solutions. So far, many contributions have been done to
find consensus clustering. One of the major problems in clustering
ensembles is the consensus function. In this paper, firstly, we
introduce clustering ensembles, representation of multiple partitions,
its challenges and present taxonomy of combination algorithms.
Secondly, we describe consensus functions in clustering ensembles
including Hypergraph partitioning, Voting approach, Mutual
information, Co-association based functions and Finite mixture
model, and next explain their advantages, disadvantages and
computational complexity. Finally, we compare the characteristics of
clustering ensembles algorithms such as computational complexity,
robustness, simplicity and accuracy on different datasets in previous
techniques.
Abstract: The design requirements for successful human
accommodation in urban spaces are well known; and the range of
facilities available for meeting urban water quality and quantity
requirements is also well established. Their competing requirements
must be reconciled in order for urban spaces to be successful for
both. This paper outlines the separate human and water imperatives
and their interactions in urban spaces. Stormwater management
facilities- relative potential contributions to urban spaces are
contrasted, and design choices for achieving those potentials are
described. This study uses human success of urban space as the
evaluative criterion of stormwater amenity: human values call on
stormwater facilities to contribute to successful human spaces.
Placing water-s contribution under the overall idea of successful
urban space is an evolution from previous subjective evaluations.
The information is based on photographs and notes from
approximately 1,000 stormwater facilities and urban sites collected
during the last 35 years in North America and overseas, and the
author-s experience on multi-disciplinary design teams. This
conceptual study combines the disciplinary roles of engineering,
landscape architecture, and sociology in effecting successful urban
design.
Abstract: Unlike its conventional counterpart, Islamic principles
forbid Islamic banks to take any interest-related income and thus
makes deposits from depositors as an important source of fund for its
operational and financing. Consequently, the risk of deposit
withdrawal by depositors is an important aspect that should be wellmanaged
in Islamic banking. This paper aims to investigate factors
that influence depositors- withdrawal behavior in Islamic banks,
particularly in Malaysia, using the framework of theory of reasoned
action. A total of 368 respondents from Klang valley are involved in
the analysis. The paper finds that all the constructs variable i.e.
normative beliefs, subjective norms, behavioral beliefs, and attitude
towards behavior are perceived to be distinct by the respondents. In
addition, the structural equation model is able to verify the structural
relationships between subjective norms, attitude towards behavior
and behavioral intention. Subjective norms gives more influence to
depositors- decision on deposit withdrawal compared to attitude
towards behavior.
Abstract: Solutions for the temperature profile around a moving
heat source are obtained using both analytic and finite element
(FEM) methods. Analytic and FEM solutions are applied to study the
temperature profile in welding. A moving heat source is represented
using both point heat source and uniform distributed disc heat source
models. Analytic solutions are obtained by solving the partial
differential equation for energy conservation in a solid, and FEM
results are provided by simulating welding using the ANSYS
software. Comparison is made for quasi steady state conditions. The
results provided by the analytic solutions are in good agreement with
results obtained by FEM.
Abstract: In this paper we ultra low-voltage and high speed CMOS domino logic. For supply voltages below 500mV the delay for a ultra low-voltage NAND2 gate is aproximately 10% of a complementary CMOS inverter. Furthermore, the delay variations due to mismatch is much less than for conventional CMOS. Differential domino gates for AND/NAND and OR/NOR operation are presented.
Abstract: Weather systems use enormously complex
combinations of numerical tools for study and forecasting.
Unfortunately, due to phenomena in the world climate, such
as the greenhouse effect, classical models may become
insufficient mostly because they lack adaptation. Therefore,
the weather forecast problem is matched for heuristic
approaches, such as Evolutionary Algorithms.
Experimentation with heuristic methods like Particle Swarm
Optimization (PSO) algorithm can lead to the development of
new insights or promising models that can be fine tuned with
more focused techniques. This paper describes a PSO
approach for analysis and prediction of data and provides
experimental results of the aforementioned method on realworld
meteorological time series.
Abstract: In this paper we present a general formalism for the
establishment of the family of selective regressor affine projection
algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-RAPA),
the SR partial rank algorithm (SR-PRA), the SR binormalized
data reusing least mean squares (SR-BNDR-LMS), and the SR normalized
LMS with orthogonal correction factors (SR-NLMS-OCF)
algorithms are established by this general formalism. We demonstrate
the performance of the presented algorithms through simulations in
acoustic echo cancellation scenario.
Abstract: In recent years, many researches to mine the exploding Web world, especially User Generated Content (UGC) such as
weblogs, for knowledge about various phenomena and events in the physical world have been done actively, and also Web services
with the Web-mined knowledge have begun to be developed for
the public. However, there are few detailed investigations on how accurately Web-mined data reflect physical-world data. It must be
problematic to idolatrously utilize the Web-mined data in public Web services without ensuring their accuracy sufficiently. Therefore,
this paper introduces the simplest Web Sensor and spatiotemporallynormalized
Web Sensor to extract spatiotemporal data about a target
phenomenon from weblogs searched by keyword(s) representing the
target phenomenon, and tries to validate the potential and reliability of the Web-sensed spatiotemporal data by four kinds of granularity
analyses of coefficient correlation with temperature, rainfall, snowfall,
and earthquake statistics per day by region of Japan Meteorological
Agency as physical-world data: spatial granularity (region-s population
density), temporal granularity (time period, e.g., per day vs. per week), representation granularity (e.g., “rain" vs. “heavy rain"), and
media granularity (weblogs vs. microblogs such as Tweets).