Abstract: This study was conducted to evaluate the manganese
removal from aqueous solution using Banana peels activated carbon
(BPAC). Batch experiments have been carried out to determine the
influence of parameters such as pH, biosorbent dose, initial metal ion
concentrations and contact times on the biosorption process. From
these investigations, a significant increase in percentage removal of
manganese 97.4% is observed at pH value 5.0, biosorbent dose 0.8 g,
initial concentration 20 ppm, temperature 25 ± 2°C, stirring rate 200
rpm and contact time 2h. The equilibrium concentration and the
adsorption capacity at equilibrium of the experimental results were
fitted to the Langmuir and Freundlich isotherm models; the Langmuir
isotherm was found to well represent the measured adsorption data
implying BPAC had heterogeneous surface. A raw groundwater
samples were collected from Baharmos groundwater treatment plant
network at Embaba and Manshiet Elkanater City/District-Giza,
Egypt, for treatment at the best conditions that reached at first phase
by BPAC. The treatment with BPAC could reduce iron and
manganese value of raw groundwater by 91.4% and 97.1%,
respectively and the effect of the treatment process on the
microbiological properties of groundwater sample showed decrease
of total bacterial count either at 22°C or at 37°C to 85.7% and 82.4%,
respectively. Also, BPAC was characterized using SEM and FTIR
spectroscopy.
Abstract: Metal-enhanced Luminescence of silicon nanocrystals
(SiNCs) was determined using two different particle sizes of silver
nanoparticles (AgNPs). SiNCs have been characterized by scanning
electron microscopy (SEM), high resolution transmission electron
microscopy (HRTEM), Fourier transform infrared spectroscopy
(FTIR) and X-ray photoelectron spectroscopy (XPS). It is found that
the SiNCs are crystalline with an average diameter of 65 nm and FCC
lattice. AgNPs were synthesized using photochemical reduction of
AgNO3 with sodium dodecyl sulphate (SDS). The enhanced
luminescence of SiNCs by AgNPs was evaluated by confocal Raman
microspectroscopy. Enhancement up to x9 and x3 times were
observed for SiNCs that mixed with AgNPs which have an average
particle size of 100 nm and 30 nm, respectively. Silver NPs-enhanced
luminescence of SiNCs occurs as a result of the coupling between the
excitation laser light and the plasmon bands of AgNPs; thus this
intense field at AgNPs surface couples strongly to SiNCs.
Abstract: Heavy metals are one of the major groups of
contaminants in the environment and many of them are toxic even at
very low concentration in plants and animals. However, some metals
play important roles in the biological function of many enzymes in
living organisms. Metals such as zinc, iron, and cooper are important
for survival and activity of enzymes in plants, however heavy metals
can inhibit enzyme which is responsible for defense system of plants.
Polyphenol oxidase (PPO) is a copper-containing metalloenzyme
which is responsible for enzymatic browning reaction of plants.
Enzymatic browning is a major problem for the handling of
vegetables and fruits in food industry. It can be increased and
effected with many different futures such as metals in the nature and
ground. In the present work, PPO was isolated and characterized
from green leaves of red poppy plant (Papaverr hoeas). Then, the
effect of some known antibrowning agents which can form
complexes with metals and metals were investigated on the red poppy
PPO activity. The results showed that glutathione was the most
potent inhibitory effect on PPO activity. Cu(II) and Fe(II) metals
increased the enzyme activities however, Sn(II) had the maximum
inhibitory effect and Zn(II) and Pb(II) had no significant effect on the
enzyme activity. In order to reduce the effect of heavy metals, the
effects of metal-antibrowning agent complexes on the PPO activity
were determined. EDTA and metal complexes had no significant
effect on the enzyme. L-ascorbic acid and metal complexes decreased
but L-ascorbic acid-Cu(II)-complex had no effect. Glutathione–metal
complexes had the best inhibitory effect on Red poppy leaf PPO
activity.
Abstract: The purpose of this research was to identify factors
that influenced the success of e-commerce implementation within
SMEs businesses. In order to achieve the objectives of this research,
the researcher collected data from random firms in Thailand, both the
users and those who are not using the e-commerce. The data was
comprised of the results of 310 questionnaires, as well as 10
interviews with owner/managers of businesses who are currently
using e-commerce successfully. The data were analyzed by using
descriptive statistics, which included frequency, percentages, mean,
and the standard deviation of pertinent factors. Independent t-test and
one-way ANOVA test were also used. The findings of this research
revealed that 50% of all the firms surveyed had e-commerce website,
whereas, over 20% of all firms surveyed had developing an ecommerce
strategy. The result findings also indicate that
organizational factors, technological factors and environment factors
as significant factors effecting success of e-commerce
implementation in SMEs. From the hypotheses testing, the findings
revealed that the different level of support use ecommerce by
owner/manager had different success in e-commerce implementation.
Moreover, the difference in e-commerce management approach
affected the success in terms of higher total sales for the business or
higher number of retained or returning customers.
Abstract: Cognitive Radio is a turning out technology that
empowers viable usage of the spectrum. Energy Detector-based
Sensing is the most broadly utilized spectrum sensing strategy.
Besides, it's a lot of generic as receivers doesn't would like any
information on the primary user's signals, channel data, of even the
sort of modulation. This paper puts forth the execution of energy
detection sensing for AM (Amplitude Modulated) signal at 710 KHz,
FM (Frequency Modulated) signal at 103.45 MHz (local station
frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz.
The OFDM/OFDMA based WiMAX physical layer with
convolutional channel coding is actualized utilizing USRP N210
(Universal Software Radio Peripheral) and GNU Radio based
Software Defined Radio (SDR). Test outcomes demonstrated the
BER (Bit Error Rate) augmentation with channel noise and BER
execution is dissected for different Eb/N0 (the energy per bit to noise
power spectral density ratio) values.
Abstract: This paper addresses the reduction of peak to average
power ratio (PAPR) for the OFDM in Mobile-WiMAX physical layer
(PHY) standard. In the process, the best achievable PAPR of 0 dB is
found for the OFDM spectrum using phase modulation technique
which avoids the nonlinear distortion. The performance of the
WiMAX PHY standard is handled by the software defined radio
(SDR) prototype in which GNU Radio and USRP N210 employed as
software and hardware platforms respectively. It is also found that
BER performance is shown for different coding and different
modulation schemes. To empathize wireless propagation in specific
environments, a sliding correlator wireless channel sounding system
is designed by using SDR testbed.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: Pomegranate (Punica granatum L.) is an ancient fruit of great medical interest and rich source of antioxidants. Pesticides as dimethoate play a crucial role in the occurrence many diseases in plants, animal and human. Therefore the ability of Pomegranate (Punica granatum L.) to alleviate hepatotoxicity induced by organophosphate pesticide dimethoate was investigated. Albino male rats were divided randomly into 4 groups and kept at 7 animals per group in an environmentally controlled condition for 6 weeks. The first group was served as a control group (basal diet), the second group fed on basal diet supplemented with 5% freeze dried pomegranate seeds, the third group fed on 20 ppm dimethoate contaminated diet and the last group fed on dimethoate contaminated diet supplemented with 5% freeze dried pomegranate seeds. The results revealed that administration of dimethoate caused high significant increased in liver functions: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) activities as well as lipid peroxide (malonaldhyde, MDA); on the other hand high significant decreased on glutathione (GSH), glutathione peroxidase (GPx), albumin and total protein were observed. However addition of 5% freeze dried pomegranate seeds significantly improved all previously mentioned parameters. These results indicate the dimethoate induced hepatotoxicity and highlight the protective effect of pomegranate seeds as a potential protective agent against dimethoate induced hepatotoxicity. This may be attributed to the powerful antioxidants (polyphenols, total phenols, and total flavonoids) which present in high levels in pomegranate as well as improving the immunity by activation of antioxidant enzymes GSH and GPx.
Abstract: This research proposes a novel reconstruction protocol
for restoring missing surfaces and low-quality edges and shapes in
photos of artifacts at historical sites. The protocol starts with the
extraction of a cloud of points. This extraction process is based on
four subordinate algorithms, which differ in the robustness and
amount of resultant. Moreover, they use different -but
complementary- accuracy to some related features and to the way
they build a quality mesh. The performance of our proposed protocol
is compared with other state-of-the-art algorithms and toolkits. The
statistical analysis shows that our algorithm significantly outperforms
its rivals in the resultant quality of its object files used to reconstruct
the desired model.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: The generalized wave equation models various
problems in sciences and engineering. In this paper, a new three-time
level implicit approach based on cubic trigonometric B-spline for the
approximate solution of wave equation is developed. The usual finite
difference approach is used to discretize the time derivative while
cubic trigonometric B-spline is applied as an interpolating function in
the space dimension. Von Neumann stability analysis is used to
analyze the proposed method. Two problems are discussed to exhibit
the feasibility and capability of the method. The absolute errors and
maximum error are computed to assess the performance of the
proposed method. The results were found to be in good agreement
with known solutions and with existing schemes in literature.
Abstract: The objectives of this study are to find out the
approaches to promote healthy recreation activities for elderly
tourists and develop Bang Nam Phueng Floating Market to be a
health tourism attraction. The research methodology was to analyze
internal and external situations according to MP-MF and the MCSTEPS
principles.
As for the results of this study the researcher found that the
healthy recreational activities for elderly tourists could be divided in
7 groups; travelling Bang Nam Phueng Floating Market activity,
homestay relaxation, arts center platform activity, healthy massage
activity, paying homage to a Buddha image activity, herbal joss-stick
home activity, making local desserts and food activity.
Abstract: The growth of wireless devices affects the availability
of limited frequencies or spectrum bands as it has been known that
spectrum bands are a natural resource that cannot be added.
Meanwhile, the licensed frequencies are idle most of the time.
Cognitive radio is one of the solutions to solve those problems.
Cognitive radio is a promising technology that allows the unlicensed
users known as secondary users (SUs) to access licensed bands
without making interference to licensed users or primary users (PUs).
As cloud computing has become popular in recent years, cognitive
radio networks (CRNs) can be integrated with cloud platform. One of
the important issues in CRNs is security. It becomes a problem since
CRNs use radio frequencies as a medium for transmitting and CRNs
share the same issues with wireless communication systems. Another
critical issue in CRNs is performance. Security has adverse effect to
performance and there are trade-offs between them. The goal of this
paper is to investigate the performance related to security trade-off in
CRNs with supporting cloud platforms. Furthermore, Queuing
Network Models with preemptive resume and preemptive repeat
identical priority are applied in this project to measure the impact of
security to performance in CRNs with or without cloud platform. The
generalized exponential (GE) type distribution is used to reflect the
bursty inter-arrival and service times at the servers. The results show
that the best performance is obtained when security is disabled and
cloud platform is enabled.
Abstract: Despite four years of study in the tourism industry, the
Bachelor’s graduates cannot perform their jobs as experienced tour
guides. This research aimed to develop French teaching and studying
for Tourism with two main purposes: to analyze ‘Moves’ used in oral
presentations at tourist attraction; and to study content in guiding
presentations or 'Guide Speak'. The study employed audio recording
of these presentations as an interview method in authentic situations,
having four tour guides as respondents and information providers.
The data was analyzed via moves and content analysis. The results
found that there were eight Moves used; namely, Welcoming,
Introducing oneself, Drawing someone’s attention, Giving
information, Explaining, Highlighting, Persuading and Saying
goodbye. In terms of content, the information being presented
covered the outstanding characteristics of the places and wellintegrated
with other related content. The findings were used as
guidelines for curriculum development; in particular, the core content
and the presentation forming the basis for students to meet the
standard requirements of the labor-market and professional schemes.
Abstract: Consumer-to-Consumer (C2C) E-commerce has been
growing at a very high speed in recent years. Since identical or
nearly-same kinds of products compete one another by relying on
keyword search in C2C E-commerce, some sellers describe their
products with spam keywords that are popular but are not related to
their products. Though such products get more chances to be retrieved
and selected by consumers than those without spam keywords,
the spam keywords mislead the consumers and waste their time.
This problem has been reported in many commercial services like
ebay and taobao, but there have been little research to solve this
problem. As a solution to this problem, this paper proposes a method
to classify whether keywords of a product are spam or not. The
proposed method assumes that a keyword for a given product is
more reliable if the keyword is observed commonly in specifications
of products which are the same or the same kind as the given
product. This is because that a hierarchical category of a product
in general determined precisely by a seller of the product and so is
the specification of the product. Since higher layers of the hierarchical
category represent more general kinds of products, a reliable degree
is differently determined according to the layers. Hence, reliable
degrees from different layers of a hierarchical category become
features for keywords and they are used together with features only
from specifications for classification of the keywords. Support Vector
Machines are adopted as a basic classifier using the features, since
it is powerful, and widely used in many classification tasks. In
the experiments, the proposed method is evaluated with a golden
standard dataset from Yi-han-wang, a Chinese C2C E-commerce,
and is compared with a baseline method that does not consider
the hierarchical category. The experimental results show that the
proposed method outperforms the baseline in F1-measure, which
proves that spam keywords are effectively identified by a hierarchical
category in C2C E-commerce.
Abstract: In this paper the issue of dimensionality reduction is
investigated in finger vein recognition systems using kernel Principal
Component Analysis (KPCA). One aspect of KPCA is to find the
most appropriate kernel function on finger vein recognition as there
are several kernel functions which can be used within PCA-based
algorithms. In this paper, however, another side of PCA-based
algorithms -particularly KPCA- is investigated. The aspect of
dimension of feature vector in PCA-based algorithms is of
importance especially when it comes to the real-world applications
and usage of such algorithms. It means that a fixed dimension of
feature vector has to be set to reduce the dimension of the input and
output data and extract the features from them. Then a classifier is
performed to classify the data and make the final decision. We
analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in
this paper and investigate the optimal feature extraction dimension in
finger vein recognition using KPCA.
Abstract: The construction of a new airport or the extension of
an existing one requires massive investments and many times public
private partnerships were considered in order to make feasible such
projects. One characteristic of these projects is uncertainty with
respect to financial and environmental impacts on the medium to long
term. Another one is the multistage nature of these types of projects.
While many airport development projects have been a success, some
others have turned into a nightmare for their promoters.
This communication puts forward a new approach for airport
investment risk assessment. The approach takes explicitly into
account the degree of uncertainty in activity levels prediction and
proposes milestones for the different stages of the project for
minimizing risk. Uncertainty is represented through fuzzy dual theory
and risk management is performed using dynamic programming. An
illustration of the proposed approach is provided.
Abstract: Multipotent mesenchymal stromal cells (MSCs)
possess immunomodulatory properties. The effect of MSCs on the
crucial cellular immunity compartment – T-cells is of a special
interest. It is known that MSC tissue niche and expected milieu of
their interaction with T- cells are characterized by low oxygen
concentration, whereas the in vitro experiments usually are carried
out at a much higher ambient oxygen (20%). We firstly evaluated
immunomodulatory effects of MSCs on T-cells at tissue-related
oxygen (5%) after interaction implied cell-to-cell contacts and
paracrine factors only. It turned out that MSCs under reduced oxygen
can effectively suppress the activation and proliferation of PHAstimulated
T-cells and can provoke decrease in the production of
proinflammatory and increase in anti-inflammatory cytokines. In
hypoxia some effects were amplified (inhibition of proliferation, antiinflammatory
cytokine profile shift). This impact was more evident
after direct cell-to-cell interaction; lack of intercellular contacts could
revoke the potentiating effect of hypoxia.
Abstract: Analysis of real life problems often results in linear
systems of equations for which solutions are sought. The method to
employ depends, to some extent, on the properties of the coefficient
matrix. It is not always feasible to solve linear systems of equations
by direct methods, as such the need to use an iterative method
becomes imperative. Before an iterative method can be employed
to solve a linear system of equations there must be a guaranty that
the process of solution will converge. This guaranty, which must
be determined apriori, involve the use of some criterion expressible
in terms of the entries of the coefficient matrix. It is, therefore,
logical that the convergence criterion should depend implicitly on the
algebraic structure of such a method. However, in deference to this
view is the practice of conducting convergence analysis for Gauss-
Seidel iteration on a criterion formulated based on the algebraic
structure of Jacobi iteration. To remedy this anomaly, the Gauss-
Seidel iteration was studied for its algebraic structure and contrary
to the usual assumption, it was discovered that some property of the
iteration matrix of Gauss-Seidel method is only diagonally dominant
in its first row while the other rows do not satisfy diagonal dominance.
With the aid of this structure we herein fashion out an improved
version of Gauss-Seidel iteration with the prospect of enhancing
convergence and robustness of the method. A numerical section is
included to demonstrate the validity of the theoretical results obtained
for the improved Gauss-Seidel method.