Abstract: The concentrations of aliphatic and polycyclic aromatic hydrocarbons (PAH) were determined in atmospheric aerosol samples collected at a rural site in Hungary (K-puszta, summer 2008), a boreal forest (Hyytiälä,
April 2007) and a polluted rural area in Italy (San Pietro Capofiume, Po Valley, April 2008). A clear distinction between “clean" and “polluted" periods was observed. Concentrations obtained for Hyytiälä are significantly lower than those for the other two sites. Source reconciliation was performed using diagnostic parameters, such as the carbon preference index and ratios between PAH. The presence of an unresolved complex mixture of hydrocarbons, especially for the Finnish and Italian samples, is indicative of petrogenic inputs. In K-puszta, the aliphatic hydrocarbons are dominated by leaf wax n-alkanes. The long range transport of anthropogenic pollution contributed to the Finnish aerosol. Industrial activities and vehicular emissions represent major sources in San Pietro Capofiume. PAH in K-puszta consist of both pyrogenic and petrogenic compounds.
Abstract: This paper proposes a novel frequency offset (FO) estimator for orthogonal frequency division multiplexing. Simplicity is most significant feature of this algorithm and can be repeated to achieve acceptable accuracy. Also fractional and integer part of FO is estimated jointly with use of the same algorithm. To do so, instead of using conventional algorithms that usually use correlation function, we use DFT of received signal. Therefore, complexity will be reduced and we can do synchronization procedure by the same hardware that is used to demodulate OFDM symbol. Finally, computer simulation shows that the accuracy of this method is better than other conventional methods.
Abstract: This paper describes the optimization of a complex
dairy farm simulation model using two quite different methods of
optimization, the Genetic algorithm (GA) and the Lipschitz
Branch-and-Bound (LBB) algorithm. These techniques have been
used to improve an agricultural system model developed by Dexcel
Limited, New Zealand, which describes a detailed representation of
pastoral dairying scenarios and contains an 8-dimensional parameter
space. The model incorporates the sub-models of pasture growth and
animal metabolism, which are themselves complex in many cases.
Each evaluation of the objective function, a composite 'Farm
Performance Index (FPI)', requires simulation of at least a one-year
period of farm operation with a daily time-step, and is therefore
computationally expensive. The problem of visualization of the
objective function (response surface) in high-dimensional spaces is
also considered in the context of the farm optimization problem.
Adaptations of the sammon mapping and parallel coordinates
visualization are described which help visualize some important
properties of the model-s output topography. From this study, it is
found that GA requires fewer function evaluations in optimization
than the LBB algorithm.
Abstract: Smith Predictor control is theoretically a good solution to the problem of controlling the time delay systems. However, it seldom gets use because it is almost impossible to find out a precise mathematical model of the practical system and very sensitive to uncertain system with variable time-delay. In this paper is concerned with a design method of smith predictor for temperature control system by Coefficient Diagram Method (CDM). The simulation results show that the control system with smith predictor design by CDM is stable and robust whilst giving the desired time domain system performance.
Abstract: The aim of the study was to identify seat belt wearing
factor among road users in Malaysia. Evidence-based approach
through in-depth crash investigation was utilised to determine the
intended objectives. The objective was scoped into crashes
investigated by Malaysian Institute of Road Safety Research
(MIROS) involving passenger vehicles within 2007 and 2010. Crash
information of a total of 99 crash cases involving 240 vehicles and
864 occupants were obtained during the study period. Statistical test
and logistic regression analysis have been performed. Results of the
analysis revealed that gender, seat position and age were associated
with seat belt wearing compliance in Malaysia. Males are 97.6%
more likely to wear seat belt compared to females (95% CI 1.317 to
2.964). By seat position, the finding indicates that frontal occupants
were 82 times more likely to be wearing seat belt (95% CI 30.199 to
225.342) as compared to rear occupants. It is also important to note
that the odds of seat belt wearing increased by about 2.64% (95% CI
1.0176 to 1.0353) for every one year increase in age. This study is
essential in understanding the Malaysian tendency in belting up
while being occupied in a vehicle. The factors highlighted in this
study should be emphasized in road safety education in order to
increase seat belt wearing rate in this country and ultimately in
preventing deaths due to road crashes.
Abstract: This paper proposes a low power SRAM based on
five transistor SRAM cell. Proposed SRAM uses novel word-line
decoding such that, during read/write operation, only selected cell
connected to bit-line whereas, in conventional SRAM (CV-SRAM),
all cells in selected row connected to their bit-lines, which in turn
develops differential voltages across all bit-lines, and this makes
energy consumption on unselected bit-lines. In proposed SRAM
memory array divided into two halves and this causes data-line
capacitance to reduce. Also proposed SRAM uses one bit-line and
thus has lower bit-line leakage compared to CV-SRAM.
Furthermore, the proposed SRAM incurs no area overhead, and has
comparable read/write performance versus the CV-SRAM.
Simulation results in standard 0.25μm CMOS technology shows in
worst case proposed SRAM has 80% smaller dynamic energy
consumption in each cycle compared to CV-SRAM. Besides, energy
consumption in each cycle of proposed SRAM and CV-SRAM
investigated analytically, the results of which are in good agreement
with the simulation results.
Abstract: To reduce the carbon dioxide emission into the
atmosphere, adsorption is believed to be one of the most attractive
methods for post-combustion treatment of flue gas. In this work,
activated carbon (AC) was modified by polyethylenimine (PEI) via
impregnation in order to enhance CO2 adsorption capacity. The
adsorbents were produced at 0.04, 0.16, 0.22, 0.25, and 0.28 wt%
PEI/AC. The adsorption was carried out at a temperature range from
30 °C to 75 °C and five different gas pressures up to 1 atm. TG-DTA,
FT-IR, UV-visible spectrometer, and BET were used to characterize
the adsorbents. Effects of PEI loading on the AC for the CO2
adsorption were investigated. Effectiveness of the adsorbents on the
CO2 adsorption including CO2 adsorption capacity and adsorption
temperature was also investigated. Adsorption capacities of CO2 were
enhanced with the increase in the amount of PEI from 0.04 to 0.22
wt% PEI before the capacities decreased onwards from0.25 wt% PEI
at 30 °C. The 0.22 wt% PEI/AC showed higher adsorption capacity
than the AC for adsorption at 50 °C to 75 °C.
Abstract: In present communication, we have developed the
suitable constraints for the given the mean codeword length and the
measures of entropy. This development has proved that Renyi-s
entropy gives the minimum value of the log of the harmonic mean
and the log of power mean. We have also developed an important
relation between best 1:1 code and the uniquely decipherable code by
using different measures of entropy.
Abstract: Due to the environmental and price issues of current
energy crisis, scientists and technologists around the globe are
intensively searching for new environmentally less-impact form of
clean energy that will reduce the high dependency on fossil fuel.
Particularly hydrogen can be produced from biomass via thermochemical
processes including pyrolysis and gasification due to the
economic advantage and can be further enhanced through in-situ
carbon dioxide removal using calcium oxide. This work focuses on
the synthesis and development of the flowsheet for the enhanced
biomass gasification process in PETRONAS-s iCON process
simulation software. This hydrogen prediction model is conducted at
operating temperature between 600 to 1000oC at atmospheric
pressure. Effects of temperature, steam-to-biomass ratio and
adsorbent-to-biomass ratio were studied and 0.85 mol fraction of
hydrogen is predicted in the product gas. Comparisons of the results
are also made with experimental data from literature. The
preliminary economic potential of developed system is RM 12.57 x
106 which equivalent to USD 3.77 x 106 annually shows economic
viability of this process.
Abstract: Tread design has evolved over the years to achieve the common tread pattern used in current vehicles. However, to meet safety and comfort requirements, tread design considers more than one design factor. Tread design must consider the grip and drainage, and the manner in which to reduce rolling noise, which is one of the main factors considered by manufacturers. The main objective of this study was the application the computational fluid dynamics (CFD) technique to simulate the contact surface of the tire and ground. The results demonstrated an air-Pumping and large pressure drop effect in the process of contact surface. The results also revealed that the pressure can be used to analyze sound pressure level (SPL).
Abstract: Clustering large populations is an important problem
when the data contain noise and different shapes. A good clustering
algorithm or approach should be efficient enough to detect clusters
sensitively. Besides space complexity, time complexity also gains
importance as the size grows. Using hierarchies we developed a new
algorithm to split attributes according to the values they have and
choosing the dimension for splitting so as to divide the database
roughly into equal parts as much as possible. At each node we
calculate some certain descriptive statistical features of the data
which reside and by pruning we generate the natural clusters with a
complexity of O(n).
Abstract: A study was undertaken to assess the potential of an
Algal Turf Scrubber to remove nitrogen from aquaculture effluent to
reduce environmental pollution. High total ammonia nitrogen
concentrations were introduced to an Algal Turf Scrubber developed
under varying hydraulic surface loading rates of African catfish
(Clarius gariepinus) effluent in a recirculating aquaculture system.
Nutrient removal rates were not affected at total suspended solids
concentration of up to 0.04g TSS/l (P > 0.05). Nitrogen removal
rates 0.93-0.99g TAN/m²/d were recorded at very high loading rates
3.76-3.81 g TAN/m²/d. Total ammonia removal showed ½ order
kinetics between 1.6 to 2.3mg/l Total Ammonia Nitrogen
concentrations. Nitrogen removal increased with its loading, which
increased with hydraulic surface loading rate. Total Ammonia
Nitrogen removal by Algal turf scrubber was higher than reported
values for fluidized bed filters and trickling filters. The algal turf
scrubber also effectively removed nitrate thereby reducing the need
for water exchange.
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: This paper attempts to establish the fact that Multi
State Network Classification is essential for performance
enhancement of Transport protocols over Satellite based Networks. A
model to classify Multi State network condition taking into
consideration both congestion and channel error is evolved. In order
to arrive at such a model an analysis of the impact of congestion and
channel error on RTT values has been carried out using ns2. The
analysis results are also reported in the paper. The inference drawn
from this analysis is used to develop a novel statistical RTT based
model for multi state network classification.
An Adaptive Multi State Proactive Transport Protocol consisting
of Proactive Slow Start, State based Error Recovery, Timeout Action
and Proactive Reduction is proposed which uses the multi state
network state classification model. This paper also confirms through
detail simulation and analysis that a prior knowledge about the
overall characteristics of the network helps in enhancing the
performance of the protocol over satellite channel which is
significantly affected due to channel noise and congestion.
The necessary augmentation of ns2 simulator is done for
simulating the multi state network classification logic. This
simulation has been used in detail evaluation of the protocol under
varied levels of congestion and channel noise. The performance
enhancement of this protocol with reference to established protocols
namely TCP SACK and Vegas has been discussed. The results as
discussed in this paper clearly reveal that the proposed protocol
always outperforms its peers and show a significant improvement in
very high error conditions as envisaged in the design of the protocol.
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: Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.
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: The model-based approach to user interface design relies on developing separate models that are capturing various aspects about users, tasks, application domain, presentation and dialog representations. This paper presents a task modeling approach for user interface design and aims at exploring the mappings between task, domain and presentation models. The basic idea of our approach is to identify typical configurations in task and domain models and to investigate how they relate each other. A special emphasis is put on application-specific functions and mappings between domain objects and operational task structures. In this respect, we will distinguish between three layers in the task decomposition: a functional layer, a planning layer, and an operational layer.
Abstract: This paper describes a complex energy signal model
that is isomorphic with digital human fingerprint images. By using
signal models, the problem of fingerprint matching is transformed
into the signal processing problem of finding a correlation between
two complex signals that differ by phase-rotation and time-scaling. A
technique for minutiae matching that is independent of image
translation, rotation and linear-scaling, and is resistant to missing
minutiae is proposed. The method was tested using random data
points. The results show that for matching prints the scaling and
rotation angles are closely estimated and a stronger match will have a
higher correlation.