Abstract: The effect of seed inoculation by VA- mycorrhiza and
different levels of phosphorus fertilizer on growth and yield of
sunflower (Azargol cultivar) was studied in experiment farm of
Islamic Azad University, Karaj Branch during 2008 growing season.
The experiment treatments were arranged in factorial based on a
complete randomized block design with three replications. Four
phosphorus fertilizer levels of 25%, 50% 75% and 100% P
recommended with two levels of Mycorrhiza: with and without
Mycorrhiza (control) were assigned in a factorial combination.
Results showed that head diameter, number of seeds in head, seed
yield and oil yield were significantly higher in inoculated plants than
in non-inoculated plants. Head diameter, number of seeds in head,
1000 seeds weight, biological yield, seed yield and oil yield increased
with increasing P level above 75% P recommended in non-inoculated
plants, whereas no significant difference was observed between 75%
and 100% P recommended. The positive effect of mycorrhizal
inoculation decreased with increasing P levels due to decreased
percent root colonization at higher P levels. According to the results
of this experiment, application of mycorrhiza in present of 50% P
recommended had an appropriate performance and could increase
seed yield and oil production to an acceptable level, so it could be
considered as a suitable substitute for chemical phosphorus fertilizer
in organic agricultural systems.
Abstract: An adaptive software reliability prediction model
using evolutionary connectionist approach based on Recurrent Radial
Basis Function architecture is proposed. Based on the currently
available software failure time data, Fuzzy Min-Max algorithm is
used to globally optimize the number of the k Gaussian nodes. The
corresponding optimized neural network architecture is iteratively
and dynamically reconfigured in real-time as new actual failure time
data arrives. The performance of our proposed approach has been
tested using sixteen real-time software failure data. Numerical results
show that our proposed approach is robust across different software
projects, and has a better performance with respect to next-steppredictability
compared to existing neural network model for failure
time prediction.
Abstract: Many corporations are seriously concerned about
security of networks and therefore, their network supervisors are still
reluctant to install WLANs. In this regards, the IEEE802.11i standard
was developed to address the security problems, even though the
mistrust of the wireless LAN technology is still existing. The thought
was that the best security solutions could be found in open standards
based technologies that can be delivered by Virtual Private
Networking (VPN) being used for long time without addressing any
security holes for the past few years. This work, addresses this issue
and presents a simulated wireless LAN of IEEE802.11g protocol, and
analyzes impact of integrating Virtual Private Network technology to
secure the flow of traffic between the client and the server within the
LAN, using OPNET WLAN utility. Two Wireless LAN scenarios
have been introduced and simulated. These are based on normal
extension to a wired network and VPN over extension to a wired
network. The results of the two scenarios are compared and indicate
the impact of improving performance, measured by response time
and load, of Virtual Private Network over wireless LAN.
Abstract: Software maintenance and mainly software
comprehension pose the largest costs in the software lifecycle. In
order to assess the cost of software comprehension, various
complexity measures have been proposed in the literature. This paper
proposes new cognitive-spatial complexity measures, which combine
the impact of spatial as well as architectural aspect of the software to
compute the software complexity. The spatial aspect of the software
complexity is taken into account using the lexical distances (in
number of lines of code) between different program elements and the
architectural aspect of the software complexity is taken into
consideration using the cognitive weights of control structures
present in control flow of the program. The proposed measures are
evaluated using standard axiomatic frameworks and then, the
proposed measures are compared with the corresponding existing
cognitive complexity measures as well as the spatial complexity
measures for object-oriented software. This study establishes that the
proposed measures are better indicators of the cognitive effort
required for software comprehension than the other existing
complexity measures for object-oriented software.
Abstract: Dexamethasone (Dex) is a synthetic glucocorticoid
that is used in therapy. However prolonged treatments with high
doses are often required. This causes side effects that interfere with
the activity of several endocrine systems, including the gonadotropic
axis.
The aim of our study is to determine the effect of Dex on testicular
function in prepubertal Wistar rats.
Newborn Wistar rats are submitted to intraperitoneal injection of
Dex (1μg of Dex dissolved in NaCl 0.9% / 5g bw) for 20 days and
then sacrificed at the age of 40days. A control group received NaCl
0.9%. The rat is weighed daily. The plasmatic levels of testosterone,
LH and FSH were measured by radioimmunoassay. A histomorphometric
study was performed on sections of testis.
Treated groups showed a significant decrease in body weight (p
Abstract: Understanding the number of people and the flow of
the persons is useful for efficient promotion of the institution
managements and company-s sales improvements. This paper
introduces an automated method for counting passerby using virtualvertical
measurement lines. The process of recognizing a passerby is
carried out using an image sequence obtained from the USB camera.
Space-time image is representing the human regions which are
treated using the segmentation process. To handle the problem of
mismatching, different color space are used to perform the template
matching which chose automatically the best matching to determine
passerby direction and speed. A relation between passerby speed and
the human-pixel area is used to distinguish one or two passersby. In
the experiment, the camera is fixed at the entrance door of the hall in
a side viewing position. Finally, experimental results verify the
effectiveness of the presented method by correctly detecting and
successfully counting them in order to direction with accuracy of
97%.
Abstract: Sleep stage scoring is the process of classifying the
stage of the sleep in which the subject is in. Sleep is classified into
two states based on the constellation of physiological parameters.
The two states are the non-rapid eye movement (NREM) and the
rapid eye movement (REM). The NREM sleep is also classified into
four stages (1-4). These states and the state wakefulness are
distinguished from each other based on the brain activity. In this
work, a classification method for automated sleep stage scoring
based on a single EEG recording using wavelet packet decomposition
was implemented. Thirty two ploysomnographic recording from the
MIT-BIH database were used for training and validation of the
proposed method. A single EEG recording was extracted and
smoothed using Savitzky-Golay filter. Wavelet packets
decomposition up to the fourth level based on 20th order Daubechies
filter was used to extract features from the EEG signal. A features
vector of 54 features was formed. It was reduced to a size of 25 using
the gain ratio method and fed into a classifier of regression trees. The
regression trees were trained using 67% of the records available. The
records for training were selected based on cross validation of the
records. The remaining of the records was used for testing the
classifier. The overall correct rate of the proposed method was found
to be around 75%, which is acceptable compared to the techniques in
the literature.
Abstract: The purpose of this research is to develop and apply the
RSCMAC to enhance the dynamic accuracy of Global Positioning
System (GPS). GPS devices provide services of accurate positioning,
speed detection and highly precise time standard for over 98% area on
the earth. The overall operation of Global Positioning System includes
24 GPS satellites in space; signal transmission that includes 2
frequency carrier waves (Link 1 and Link 2) and 2 sets random
telegraphic codes (C/A code and P code), on-earth monitoring stations
or client GPS receivers. Only 4 satellites utilization, the client position
and its elevation can be detected rapidly. The more receivable
satellites, the more accurate position can be decoded. Currently, the
standard positioning accuracy of the simplified GPS receiver is greatly
increased, but due to affected by the error of satellite clock, the
troposphere delay and the ionosphere delay, current measurement
accuracy is in the level of 5~15m. In increasing the dynamic GPS
positioning accuracy, most researchers mainly use inertial navigation
system (INS) and installation of other sensors or maps for the
assistance. This research utilizes the RSCMAC advantages of fast
learning, learning convergence assurance, solving capability of
time-related dynamic system problems with the static positioning
calibration structure to improve and increase the GPS dynamic
accuracy. The increasing of GPS dynamic positioning accuracy can be
achieved by using RSCMAC system with GPS receivers collecting
dynamic error data for the error prediction and follows by using the
predicted error to correct the GPS dynamic positioning data. The
ultimate purpose of this research is to improve the dynamic positioning
error of cheap GPS receivers and the economic benefits will be
enhanced while the accuracy is increased.
Abstract: We consider a Principal-Agent model with the
Principal being a seller who does not know perfectly how much the
buyer (the Agent) is willing to pay for the good. The buyer-s
preferences are hence his private information. The model corresponds
to the nonlinear pricing problem of Maskin and Riley. We assume
there are three types of Agents. The model is solved using
“informational rents" as variables. In the last section we present the
main characteristics of the optimal contracts in asymmetric
information and some possible extensions of the model.
Abstract: The use of renewable energy sources incl. biogas has become topical in accordance with the increasing demand for energy, decrease of fossil energy resources and the efforts to reduce greenhouse gas emissions as well as to increase energy independence from the territories where fossil energy resources are available.
As the technologies of biogas production from agricultural biomass develop, risk assessment and risk management become necessary for farms producing such a renewable energy. The need for risk assessments has become particularly topical when discussions on changing the biogas policy in the EU take place, which may influence the development of the sector in the future, as well as the operation of existing biogas facilities and their income level.
The current article describes results of the risk assessment for farms producing biomass from agriculture biomass in Latvia, the risk assessment system included 24 risks, that affect the whole biogas production process and the obtained results showed the high significance of political and production risks.
Abstract: Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Abstract: Information and communication service providers
(ICSP) that are significant in size and provide Internet-based services
take administrative, technical, and physical protection measures via
the information security check service (ISCS). These protection
measures are the minimum action necessary to secure the stability and
continuity of the information and communication services (ICS) that
they provide. Thus, information assets are essential to providing ICS,
and deciding the relative importance of target assets for protection is a
critical procedure. The risk analysis model designed to decide the
relative importance of information assets, which is described in this
study, evaluates information assets from many angles, in order to
choose which ones should be given priority when it comes to
protection. Many-sided risk analysis (MSRS) grades the importance of
information assets, based on evaluation of major security check items,
evaluation of the dependency on the information and communication
facility (ICF) and influence on potential incidents, and evaluation of
major items according to their service classification, in order to
identify the ISCS target. MSRS could be an efficient risk analysis
model to help ICSPs to identify their core information assets and take
information protection measures first, so that stability of the ICS can
be ensured.
Abstract: Problem solving has traditionally been one of the principal research areas for artificial intelligence. Yet, although artificial intelligence reasoning techniques have been employed in several product support systems, the benefit of integrating product support, knowledge engineering, and problem solving, is still unclear. This paper studies the synergy of these areas and proposes a knowledge engineering framework that integrates product support systems and artificial intelligence techniques. The framework includes four spaces; the data, problem, hypothesis, and solution ones. The data space incorporates the knowledge needed for structured reasoning to take place, the problem space contains representations of problems, and the hypothesis space utilizes a multimodal reasoning approach to produce appropriate solutions in the form of virtual documents. The solution space is used as the gateway between the system and the user. The proposed framework enables the development of product support systems in terms of smaller, more manageable steps while the combination of different reasoning techniques provides a way to overcome the lack of documentation resources.
Abstract: In elevating performance in competetive sports, an
athlete must continously train in achieving maximum
performance,but needs to pay attention to recovery therapy, that is to
recover from fatigue as well as injury.The correct recovery therapy
will assist in process of recovery and helps in the training in
achieving better performace. Binahong (Anredera cordifolia) was
proven empirically by the locals in assisting speedy recovery from an
injury.Clinical research with lab animals receiving blunt trauma
injury, microscopically shown signs of: 1) redness, 2) heatiness, 3)
swelling and, 4) lack of activity. There is also microscopic indication
of: 1) infiltration of inflame cells (migration of cells to the trauma
area), 2) Cells necrosis, 3) Congestion (as a result of dead red blood
cells), 4) uedema. On administration of Binahong for 3 days, there is
a significant drop of 5% in cell inflammation, 2% increase of
fibroblast (cell membrance) count.Conclutin: Binahong do assist in
reducing cell inflammation and increase counts of cells fibroblast.
Suggestion: In helping athlete's to recover from force injury, we need
study about Binahong's roots to inflammation cell and healing of
injuried cell.
Abstract: Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In the light of the clustering data, we have verified some interactions which were not identified as core interactions in DIP and also, we have characterized some functionally unknown proteins according to the interaction data and functional correlation. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins, also to predict new interactions and to characterize functions of unknown proteins.
Abstract: The process parameters, temperature, pH and
substrate concentration, were optimized for the production of
gentamicin using Micromonouspora echinospora. Experiments were
carried out according to central composite design in response surface
method. The optimum conditions for the maximum production of
gentamicin were found to be: temperature – 31.7oC, pH – 6.8 and
substrate concentration – 3%. At these optimized conditions the
production of gentamicin was found to be – 1040 mg/L. The R2 value
of 0.9465 indicates a good fitness of the model.
Abstract: This paper reports a new approach on identifying the
individuality of persons by using parametric classification of multiple
mental thoughts. In the approach, electroencephalogram (EEG)
signals were recorded when the subjects were thinking of one or
more (up to five) mental thoughts. Autoregressive features were
computed from these EEG signals and classified by Linear
Discriminant classifier. The results here indicate that near perfect
identification of 400 test EEG patterns from four subjects was
possible, thereby opening up a new avenue in biometrics.
Abstract: The main purpose of this study is to analyze the
feelings of tourists for the service quality of the bikeway. In addition,
this study also analyzed the causal relationship between service
quality and satisfaction to visitor-s lane loyalty. In this study, the Ya
Tam San bikeway visitor-s subjects, using the designated convenience
sampling carried out the survey, a total of 651 questionnaires were
validly. Valid questionnaires after statistical analysis, the following
findings: 1. Visitor-s lane highest quality of service project: the routes
through the region weather pleasant. Lane "with health and sports," the
highest satisfaction various factors of service quality and satisfaction,
loyal between correlations exist. 4. Guided tours of bikeways, the
quality of the environment, and modeling imagery can effectively
predict visitor satisfaction. 5. Quality of bikeway, public facilities,
guided tours, and modeling imagery can effectively predict visitor
loyalty. According to the above results, the study not only makes
recommendations to the government units and the bicycle industry,
also asked the research direction for future researchers.
Abstract: ZnO nanostructures including nanowires, nanorods,
and nanoneedles were successfully deposited on GaAs substrates,
respectively, by simple two-step chemical method for the first time. A
ZnO seed layer was firstly pre-coated on the O2-plasma treated
substrate by sol-gel process, followed by the nucleation of ZnO
nanostructures through hydrothermal synthesis. Nanostructures with
different average diameter (15-250 nm), length (0.9-1.8 μm), density
(0.9-16×109 cm-2) were obtained via adjusting the growth time and
concentration of precursors. From the reflectivity spectra, we
concluded ordered and taper nanostructures were preferential for
photovoltaic applications. ZnO nanoneedles with an average diameter
of 106 nm, a moderate length of 2.4 μm, and the density of 7.2×109
cm-2 could be synthesized in the concentration of 0.04 M for 18 h.
Integrated with the nanoneedle array, the power conversion efficiency
of single junction solar cell was increased from 7.3 to 12.2%,
corresponding to a 67% improvement.
Abstract: Nanocrystalline thin film of Na0.1V2O5.nH2O xerogel
obtained by sol gel synthesis was used as gas sensor. Gas sensing
properties of different gases such as hydrogen, petroleum and
humidity were investigated. Applying XRD and TEM the size of the
nanocrystals is found to be 7.5 nm. SEM shows a highly porous
structure with submicron meter-sized voids present throughout the
sample. FTIR measurement shows different chemical groups
identifying the obtained series of gels. The sample was n-type
semiconductor according to the thermoelectric power and electrical
conductivity. It can be seen that the sensor response curves from
130oC to 150oC show a rapid increase in sensitivity for all types of
gas injection, low response values for heating period and the rapid
high response values for cooling period. This result may suggest that
this material is able to act as gas sensor during the heating and
cooling process.