Abstract: With demand for primary energy continuously
growing, search for renewable and efficient energy sources has been
high on agenda of our society. One of the most promising energy
sources is biogas technology. Residues coming from dairy industry
and milk processing could be used in biogas production; however,
low efficiency and high cost impede wide application of such
technology. One of the main problems is management and conversion
of organic residues through the anaerobic digestion process which is
characterized by acidic environment due to the low whey pH (
Abstract: This paper reports the empirical investigation on the
effect of involuntary displacement of indigenous tribes on their sociocultural
and food practices. A descriptive research design using the
quantitative approach was applied and individual of indigenous tribes
as unit of analysis. Through a self-administered survey among two
selected Malaysia indigenous tribes, one hundred fifty questionnaires
were successfully collected. With the application of descriptive and
inferential statistic some useful insights pertaining to the issue
investigated was significantly obtained. Findings revealed that
improvement on the socio-culture, economy and knowledge is
apparent on the indigenous groups’ resulted from displacement
program. Displacement also has a slight impact on indigenous
groups’ food practices. These positive indications provide significant
implications, not only for the indigenous groups themselves, but also
for the responsible authorities.
Abstract: In order to investigate the effect of Plant Growth
Promoting Rhizobacteria (PGPR) and rhizobium bacteria on grain
yield and some agronomic traits of mungbean (Vigna radiate L.), an
experiment was carried out based on randomized complete block
design with three replications in Malekshahi, Ilam province, Iran
during 2012-2013 cropping season. Experimental treatments
consisted of control treatment, inoculation with rhizobium bacteria,
rhizobium bacteria and Azotobacter, rhizobium bacteria and
Azospirillum, rhizobium bacteria and Pseudomonas, rhizobium
bacteria, Azotobacter and Azospirillum, rhizobium bacteria,
Azotobacter and Pseudomonas, rhizobium bacteria, Azospirillum and
Pseudomonas and rhizobium bacteria, Azotobacter, Azospirillum and
Pseudomonas. The results showed that the effect of PGPR and
rhizobium bacteria were significant affect on grain and its
components in mungbean plant. Grain yield significantly increased
by PGPR and rhizobium bacteria, so that the maximum grain yield
was obtained from rhizobium bacteria + Azospirillum +
Pseudomonas with the amount of 2287 kg.ha-1 as compared to
control treatment. Excessive application of chemical fertilizers causes
environmental and economic problems. That is, the overfertilization
of P and N leads to pollution due to soil erosion and runoff water, so
the use of PGPR and rhizobium bacteria can be justified due to
reduce input costs, increase in grain yield and environmental friendly.
Abstract: Business interpreting talents are in badly need for local
economic development, but currently there are problems of traditional
business interpreting training mode in China. In view of the good
opportunity for college business interpreters provided by international
trading center development in Qingdao China and with the aim of
being in line with market demand and enhancing business interpreters'
employment competitive advantage, this paper aims to explore how to
cultivate interdisciplinary business interpreting talents based on
market demand.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: Currently, biological control programs in greenhouse
crops involve the use, at the same time, several natural enemies
during the crop cycle. Also, large number of plant species grown in
greenhouses, among them, the used cultivars are also wide. However,
the cultivar effects on entomophagous species efficacy (predators and
parasitoids) have been scarcely studied. A new method had been
developed, using the factitious prey or host Ephestia kuehniella. It
allow us to evaluate, under greenhouse or controlled conditions
(semi-field), the cultivar effects on the entomophagous species
effectiveness. The work was carried out in greenhouse tomato crop. It
has been found the biological and ecological activities of predatory
species (Nesidiocoris tenuis) and egg-parasitoid (Trichogramma
achaeae) can be well represented with the use of the factitious prey
or host; being better in the former than the latter. The data found in
the trial are shown and discussed. The developed method could be
applied to evaluate new plant materials before making available to
farmers as commercial varieties, at low costs and easy use.
Abstract: The paper presents the results of clusterization by
Kohonen self-organizing maps (SOM) applied for analysis of array of
Raman spectra of multi-component solutions of inorganic salts, for
determination of types of salts present in the solution. It is
demonstrated that use of SOM is a promising method for solution of
clusterization and classification problems in spectroscopy of multicomponent
objects, as attributing a pattern to some cluster may be
used for recognition of component composition of the object.
Abstract: In this study, a comparative analysis of the approaches
associated with the use of neural network algorithms for effective
solution of a complex inverse problem – the problem of identifying
and determining the individual concentrations of inorganic salts in
multicomponent aqueous solutions by the spectra of Raman
scattering of light – is performed. It is shown that application of
artificial neural networks provides the average accuracy of
determination of concentration of each salt no worse than 0.025 M.
The results of comparative analysis of input data compression
methods are presented. It is demonstrated that use of uniform
aggregation of input features allows decreasing the error of
determination of individual concentrations of components by 16-18%
on the average.
Abstract: Color Histogram is considered as the oldest method
used by CBIR systems for indexing images. In turn, the global
histograms do not include the spatial information; this is why the
other techniques coming later have attempted to encounter this
limitation by involving the segmentation task as a preprocessing step.
The weak segmentation is employed by the local histograms while
other methods as CCV (Color Coherent Vector) are based on strong
segmentation. The indexation based on local histograms consists of
splitting the image into N overlapping blocks or sub-regions, and
then the histogram of each block is computed. The dissimilarity
between two images is reduced, as consequence, to compute the
distance between the N local histograms of the both images resulting
then in N*N values; generally, the lowest value is taken into account
to rank images, that means that the lowest value is that which helps to
designate which sub-region utilized to index images of the collection
being asked. In this paper, we make under light the local histogram
indexation method in the hope to compare the results obtained against
those given by the global histogram. We address also another
noteworthy issue when Relying on local histograms namely which
value, among N*N values, to trust on when comparing images, in
other words, which sub-region among the N*N sub-regions on which
we base to index images. Based on the results achieved here, it seems
that relying on the local histograms, which needs to pose an extra
overhead on the system by involving another preprocessing step
naming segmentation, does not necessary mean that it produces better
results. In addition to that, we have proposed here some ideas to
select the local histogram on which we rely on to encode the image
rather than relying on the local histogram having lowest distance with
the query histograms.
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: Among all FACTS devices, the unified power flow
controller (UPFC) is considered to be the most versatile device.
This is due to its capability to control all the transmission system
parameters (impedance, voltage magnitude, and phase angle). With
the growing interest in UPFC, the attention to develop a mathematical
model has increased. Several models were introduced for UPFC in
literature for different type of studies in power systems. In this paper
a novel comparison study between two dynamic models of UPFC
with their proposed control strategies.
Abstract: Researches and concerns in power quality gained
significant momentum in the field of power electronics systems over
the last two decades globally. This sudden increase in the number of
concerns over power quality problems is a result of the huge increase
in the use of non-linear loads. In this paper, power quality evaluation
of some distribution networks at Misurata - Libya has been done
using a power quality and energy analyzer (Fluke 437 Series II). The
results of this evaluation are used to minimize the problems of power
quality. The analysis shows the main power quality problems that
exist and the level of awareness of power quality issues with the aim
of generating a start point which can be used as guidelines for
researchers and end users in the field of power systems.
Abstract: This work proposes a fuzzy methodology to support
the investment decisions. While choosing among competitive
investment projects, the methodology makes ranking of projects
using the new aggregation OWA operator – AsPOWA, presented in
the environment of possibility uncertainty. For numerical evaluation
of the weighting vector associated with the AsPOWA operator the
mathematical programming problem is constructed. On the basis of
the AsPOWA operator the projects’ group ranking maximum criteria
is constructed. The methodology also allows making the most
profitable investments into several of the project using the method
developed by the authors for discrete possibilistic bicriteria problems.
The article provides an example of the investment decision-making
that explains the work of the proposed methodology.
Abstract: Sudoku is a logic-based combinatorial puzzle game
which people in different ages enjoy playing it. The challenging and
addictive nature of this game has made it a ubiquitous game. Most
magazines, newspapers, puzzle books, etc. publish lots of Sudoku
puzzles every day. These puzzles often come in different levels of
difficulty so that all people, from beginner to expert, can play the
game and enjoy it. Generating puzzles with different levels of
difficulty is a major concern of Sudoku designers. There are several
works in the literature which propose ways of generating puzzles
having a desirable level of difficulty. In this paper, we propose a
method based on constraint satisfaction problems to evaluate the
difficulty of the Sudoku puzzles. Then we propose a hill climbing
method to generate puzzles with different levels of difficulty.
Whereas other methods are usually capable of generating puzzles
with only few number of difficulty levels, our method can be used to
generate puzzles with arbitrary number of different difficulty levels.
We test our method by generating puzzles with different levels of
difficulty and having a group of 15 people solve all the puzzles and
recording the time they spend for each puzzle.
Abstract: A method is proposed for stable detection of
seismoacoustic sources in C-OTDR systems that guarantee given
upper bounds for probabilities of type I and type II errors. Properties
of the proposed method are rigorously proved. The results of
practical applications of the proposed method in a real C-OTDRsystem
are presented.
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: One of the major goals of Spoken Dialog Systems
(SDS) is to understand what the user utters.
In the SDS domain, the Spoken Language Understanding (SLU)
Module classifies user utterances by means of a pre-definite
conceptual knowledge. The SLU module is able to recognize only the
meaning previously included in its knowledge base. Due the vastity
of that knowledge, the information storing is a very expensive
process.
Updating and managing the knowledge base are time-consuming
and error-prone processes because of the rapidly growing number of
entities like proper nouns and domain-specific nouns. This paper
proposes a solution to the problem of Name Entity Recognition
(NER) applied to a SDS domain. The proposed solution attempts to
automatically recognize the meaning associated with an utterance by
using the PANKOW (Pattern based Annotation through Knowledge
On the Web) method at runtime.
The method being proposed extracts information from the Web to
increase the SLU knowledge module and reduces the development
effort. In particular, the Google Search Engine is used to extract
information from the Facebook social network.
Abstract: This paper presents a new meta-heuristic bio-inspired
optimization algorithm which is called Cuttlefish Algorithm (CFA).
The algorithm mimics the mechanism of color changing behavior of
the cuttlefish to solve numerical global optimization problems. The
colors and patterns of the cuttlefish are produced by reflected light
from three different layers of cells. The proposed algorithm considers
mainly two processes: reflection and visibility. Reflection process
simulates light reflection mechanism used by these layers, while
visibility process simulates visibility of matching patterns of the
cuttlefish. To show the effectiveness of the algorithm, it is tested with
some other popular bio-inspired optimization algorithms such as
Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and
Bees Algorithm (BA) that have been previously proposed in the
literature. Simulations and obtained results indicate that the proposed
CFA is superior when compared with these algorithms.
Abstract: This study aimed at designing and developing a
mechanical force gauge for the square watermelon mold for the first
time. It also tried to introduce the square watermelon characteristics
and its production limitations. The mechanical force gauge
performance and the product itself were also described. There are
three main designable gauge models: a. hydraulic gauge, b. strain
gauge, and c. mechanical gauge. The advantage of the hydraulic
model is that it instantly displays the pressure and thus the force
exerted by the melon. However, considering the inability to measure
forces at all directions, complicated development, high cost, possible
hydraulic fluid leak into the fruit chamber and the possible influence
of increased ambient temperature on the fluid pressure, the
development of this gauge was overruled. The second choice was to
calculate pressure using the direct force a strain gauge. The main
advantage of these strain gauges over spring types is their high
precision in measurements; but with regard to the lack of conformity
of strain gauge working range with water melon growth, calculations
were faced with problems. Finally the mechanical pressure gauge has
advantages, including the ability to measured forces and pressures on
the mold surface during melon growth; the ability to display the peak
forces; the ability to produce melon growth graph thanks to its
continuous force measurements; the conformity of its manufacturing
materials with the required physical conditions of melon growth; high
air conditioning capability; the ability to permit sunlight reaches the
melon rind (no yellowish skin and quality loss); fast and
straightforward calibration; no damages to the product during
assembling and disassembling; visual check capability of the product
within the mold; applicable to all growth environments (field,
greenhouses, etc.); simple process; low costs and so forth.
Abstract: This paper presents the performance state analysis of
Self-Excited Induction Generator (SEIG) using Artificial Bee Colony
(ABC) optimization technique. The total admittance of the induction
machine is minimized to calculate the frequency and magnetizing
reactance corresponding to any rotor speed, load impedance and
excitation capacitance. The performance of SEIG is calculated using
the optimized parameter found. The results obtained by ABC
algorithm are compared with results from numerical method. The
results obtained coincide with the numerical method results. This
technique proves to be efficient in solving nonlinear constrained
optimization problems and analyzing the performance of SEIG.