Abstract: Information regarding early onset neonatal sepsis
(EONS) pathogens may vary between regions. Global perspectives
showed Group B Streptococcal (GBS) as the most common causative
pathogens, but the widespread use of intrapartum antibiotics has
changed the pathogens pattern towards gram negative
microorganisms, especially E. coli. Objective of this study is to
describe the pathogens isolated, to assess current treatment and risk
of EONS. Records of 899 neonates born in three General Hospitals
between 2009 until 2012 were retrospectively reviewed. Proven was
found in 22 (3%) neonates. The majority was isolated with gram
positive organisms, 17 (2.3%). All grams positive and most gram
negative organisms showed sensitivity to the tested antibiotics. Only
two rare gram negative organisms showed total resistant. Male was
possible risk of proven EONS. Although proven EONS remains
uncommon in Malaysia, nonetheless, the effect of intrapartum
antibiotics still required continuous surveillance.
Abstract: There is an urgent need to develop novel
Mycobacterium tuberculosis (Mtb) drugs that are active against drug
resistant bacteria but, more importantly, kill persistent bacteria. Our
study structured based on integrated analysis of metabolic pathways,
small molecule screening and similarity Search in PubChem
Database. Metabolic analysis approaches based on Unified weighted
used for potent target selection. Our results suggest that pantothenate
synthetase (panC) and and 3-methyl-2-oxobutanoate hydroxymethyl
transferase (panB) as a appropriate drug targets. In our study, we
used pantothenate synthetase because of existence inhibitors. We
have reported the discovery of new antitubercular compounds
through ligand based approaches using computational tools.
Abstract: In a world worried about water resources with the
shadow of drought and famine looming all around, the quality of
water is as important as its quantity. The source of all concerns is the
constant reduction of per capita quality water for different uses.
Iran With an average annual precipitation of 250 mm compared to
the 800 mm world average, Iran is considered a water scarce country
and the disparity in the rainfall distribution, the limitations of
renewable resources and the population concentration in the margins
of desert and water scarce areas have intensified the problem.
The shortage of per capita renewable freshwater and its poor
quality in large areas of the country, which have saline, brackish or
hard water resources, and the profusion of natural and artificial
pollutant have caused the deterioration of water quality.
Among methods of treatment and use of these waters one can refer
to the application of membrane technologies, which have come into
focus in recent years due to their great advantages. This process is
quite efficient in eliminating multi-capacity ions; and due to the
possibilities of production at different capacities, application as
treatment process in points of use, and the need for less energy in
comparison to Reverse Osmosis processes, it can revolutionize the
water and wastewater sector in years to come. The article studied the
different capacities of water resources in the Persian Gulf and Oman
Sea watershed basins, and processes the possibility of using
nanofiltration process to treat brackish and non-conventional waters
in these basins.
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: Due to the deregulation of the Electric Supply
Industry and the resulting emergence of electricity market, the
volumes of power purchases are on the rise all over the world. In a
bid to meet the customer-s demand in a reliable and yet economic
manner, utilities purchase power from the energy market over and
above its own production. This paper aims at developing an optimal
power purchase model with two objectives viz economy and
environment ,taking various functional operating constraints such as
branch flow limits, load bus voltage magnitudes limits, unit capacity
constraints and security constraints into consideration.The price of
purchased power being an uncertain variable is modeled using fuzzy
logic. DEMO (Differential Evolution For Multi-objective
Optimization) is used to obtain the pareto-optimal solution set of the
multi-objective problem formulated. Fuzzy set theory has been
employed to extract the best compromise non-dominated solution.
The results obtained on IEEE 30 bus system are presented and
compared with that of NSGAII.
Abstract: Urban non-point source (NPS) pollution for a
residential catchment in Miri, Sarawak was investigated for two storm events in 2011. Runoff from two storm events were sampled and tested for water quality parameters including TSS, BOD5, COD,
NH3-N, NO3-N, NO2-N, P and Pb. Concentration of the water quality
parameters was found to vary significantly between storms and the pollutant of concern was found to be NO3-N, TSS, COD and Pb. Results were compared to the Interim National Water Quality
Standards for Malaysia (INWQS),and the stormwater runoff from the
study can be classified as polluted, exceeding class III water quality,
especially in terms of TSS, COD, and NH3-N with maximum EMCs
of 158, 135, and 2.17 mg/L, respectively.
Abstract: Wireless mobile communications have experienced
the phenomenal growth through last decades. The advances in
wireless mobile technologies have brought about a demand for high
quality multimedia applications and services. For such applications
and services to work, signaling protocol is required for establishing,
maintaining and tearing down multimedia sessions. The Session
Initiation Protocol (SIP) is an application layer signaling protocols,
based on request/response transaction model. This paper considers
SIP INVITE transaction over an unreliable medium, since it has been
recently modified in Request for Comments (RFC) 6026. In order to
help in assuring that the functional correctness of this modification is
achieved, the SIP INVITE transaction is modeled and analyzed using
Colored Petri Nets (CPNs). Based on the model analysis, it is
concluded that the SIP INVITE transaction is free of livelocks and
dead codes, and in the same time it has both desirable and
undesirable deadlocks. Therefore, SIP INVITE transaction should be
subjected for additional updates in order to eliminate undesirable
deadlocks. In order to reduce the cost of implementation and
maintenance of SIP, additional remodeling of the SIP INVITE
transaction is recommended.
Abstract: A mathematical model of the respiratory system is
introduced in this study. Geometrical dimensions of the respiratory
system were used to compute the acoustic properties of the
respiratory system using the electro-acoustic analogy. The effect of
the geometrical proportions of the respiratory system is observed in
the paper.
Abstract: Macrobenthos distribution along the coastal waters of
Penang National Park was studid to estimate the effect of different
environmental parameters at three stations, during six sampling
months, from June 2010 to April 2011. The aim of this survey was to
investigate different environment stress over soft bottom polychaete
community along Teluk Ketapang and Pantai Acheh (Penang
National Park) over a year period. Variations in the polychaete
community were evaluated using univariate and multivariate
methods. A total of 604 individuals were examined which was
grouped into 23 families. Family Nereidae was the most abundant
(22.68%), followed by Spionidae (22.02%), Hesionidae (12.58%),
Nephtylidae (9.27%) and Orbiniidae (8.61%). It is noticeable that
good results can only be obtained on the basis of good taxonomic
resolution. The maximum Shannon-Wiener diversity (H'=2.16) was
recorded at distance 200m and 1200m (August 2010) in Teluk
Ketapang and lowest value of diversity was found at distance 1200m
(December 2010) in Teluk Ketapang.
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: Fixed-bed slow pyrolysis experiments of rice husk
have been conducted to determine the effect of pyrolysis
temperature, heating rate, particle size and reactor length on the
pyrolysis product yields. Pyrolysis experiments were performed at
pyrolysis temperature between 400 and 600°C with a constant
heating rate of 60°C/min and particle sizes of 0.60-1.18 mm. The
optimum process conditions for maximum liquid yield from the rice
husk pyrolysis in a fixed bed reactor were also identified. The highest
liquid yield was obtained at a pyrolysis temperature of 500°C,
particle size of
1.18-1.80 mm, with a heating rate of 60°C/min in a 300 mm length
reactor. The obtained yield of, liquid, gas and solid were found be in
the range of 22.57-31.78 %, 27.75-42.26 % and 34.17-42.52 % (all
weight basics) respectively at different pyrolysis conditions. The
results indicate that the effects of pyrolysis temperature and particle
size on the pyrolysis yield are more significant than that of heating
rate and reactor length. The functional groups and chemical
compositions present in the liquid obtained at optimum conditions
were identified by Fourier Transform-Infrared (FT-IR) spectroscopy
and Gas Chromatography/ Mass Spectroscopy (GC/MS) analysis
respectively.
Abstract: A specially designed flat plate was mounted vertically
over the axial line in the wind tunnel of the Aerospace Department of
the Pusan National University. The plate is 2 m long, 0.8 m high and 8
cm thick. The measurements were performed in velocity range from
15 to 60 m/s. A sand paper turbulizer was placed close to the plate nose
to provide fully developed turbulent boundary layer over the most part
of the plate. Strain balances were mounted in the trailing part of the
plate to measure the skin friction drag over removable insertions of
0.55×0.25m2 size. A set of the insertions was designed and
manufactured: 3mm thick polished metal surface and three compliant
surfaces. The compliant surfaces were manufactured of a silicone
rubber Silastic® S2 (Dow Corning company). To modify the
viscoelastic properties of the rubber, its composition was varied: 90%
of the rubber + 10% catalyst (standard), 92.5% + 7.5% (weak), 85% +
15% (strong). Modulus of elasticity and the loss tangent were
measured accurately for these materials in the frequency range from
40 Hz to 3 KHz using the unique proposed technique.
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.
Abstract: This work concerns the topological optimization
problem for determining the optimal petroleum refinery
configuration. We are interested in further investigating and
hopefully advancing the existing optimization approaches and
strategies employing logic propositions to conceptual process
synthesis problems. In particular, we seek to contribute to this
increasingly exciting area of chemical process modeling by
addressing the following potentially important issues: (a) how the
formulation of design specifications in a mixed-logical-and-integer
optimization model can be employed in a synthesis problem to enrich
the problem representation by incorporating past design experience,
engineering knowledge, and heuristics; and (b) how structural
specifications on the interconnectivity relationships by space (states)
and by function (tasks) in a superstructure should be properly
formulated within a mixed-integer linear programming (MILP)
model. The proposed modeling technique is illustrated on a case
study involving the alternative processing routes of naphtha, in which
significant improvement in the solution quality is obtained.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: The Major Depressive Disorder has been a burden of
medical expense in Taiwan as well as the situation around the world.
Major Depressive Disorder can be defined into different categories by
previous human activities. According to machine learning, we can
classify emotion in correct textual language in advance. It can help
medical diagnosis to recognize the variance in Major Depressive
Disorder automatically. Association language incremental is the
characteristic and relationship that can discovery words in sentence.
There is an overlapping-category problem for classification. In this
paper, we would like to improve the performance in classification in
principle of no overlapping-category problems. We present an
approach that to discovery words in sentence and it can find in high
frequency in the same time and can-t overlap in each category, called
Association Language Features by its Category (ALFC).
Experimental results show that ALFC distinguish well in Major
Depressive Disorder and have better performance. We also compare
the approach with baseline and mutual information that use single
words alone or correlation measure.
Abstract: Societal security, continuity scenarios and methodological cycling approach explained in this article. Namely societal security organizational challenges ask implementation of international standards BS 25999-2 & global ISO 22300 which is a family of standards for business continuity management system. Efficient global organization system is distinguished of high entity´s complexity, connectivity & interoperability, having not only cooperative relations in a fact. Competing business have numerous participating ´enemies´, which are in apparent or hidden opponent and antagonistic roles with prosperous organization system, resulting to a crisis scene or even to a battle theatre. Organization business continuity scenarios are necessary for such ´a play´ preparedness, planning, management & overmastering in real environments.
Abstract: This study adopted previous fault patterns, results of
detection analysis, historical records and data, and experts-
experiences to establish fuzzy principles and estimate the failure
probability index of components of a power transformer. Considering
that actual parameters and limiting conditions of parameters may
differ, this study used the standard data of IEC, IEEE, and CIGRE as
condition parameters. According to the characteristics of each
condition parameter, relative degradation was introduced to reflect the
degree of influence of the factors on the transformer condition. The
method of fuzzy mathematics was adopted to determine the
subordinate function of the transformer condition. The calculation
used the Matlab Fuzzy Tool Box to select the condition parameters of
coil winding, iron core, bushing, OLTC, insulating oil and other
auxiliary components and factors (e.g., load records, performance
history, and maintenance records) of the transformer to establish the
fuzzy principles. Examples were presented to support the rationality
and effectiveness of the evaluation method of power transformer
performance conditions, as based on fuzzy comprehensive evaluation.
Abstract: In this paper an alternative visualisation approach of
the wake behind different vehicle body shapes with simplified and
fully-detailed underbody has been proposed and analysed. This
allows for a more clear distinction among the different wake regions.
This visualisation is based on a transformation of the cartesian
coordinates of a chosen wake plane to polar coordinates, using as
filter velocities lower than the freestream. This transformation
produces a polar wake plot that enables the division and
quantification of the wake in a number of sections. In this paper,
local drag has been used to visualise the drag contribution of the flow
by the different sections. Visually, a balanced wake can be observed
by the concentric behaviour of the polar plots. Alternatively,
integration of the local drag of each degree section as a ratio of the
total local drag yields a quantifiable approach of the wake uniformity,
where different sections contribute equally to the local drag, with the
exception of the wheels.
Abstract: Direct conversion of methane to methanol by partial oxidation in a thermal reactor has a poor yield of about 2% which is less than the expected economical yield of about 10%. Conventional thermal catalytic reactors have been proposed to be superseded by plasma reactors as a promising approach, due to strength of the electrical energy which can break C-H bonds of methane. Among the plasma techniques, non-thermal dielectric barrier discharge (DBD) plasma chemical process is one of the most future promising technologies in synthesizing methanol. The purpose of this paper is presenting a brief review of CH4 oxidation with O2 in DBD plasma reactors based on the recent investigations. For this reason, the effect of various parameters of reactor configuration, feed ratio, applied voltage, residence time (gas flow rate), type of applied catalyst, pressure and reactor wall temperature on methane conversion and methanol selectivity are discussed.