Abstract: MicroRNAs are an important class of gene expression
regulators that are involved in many biological processes including
embryogenesis. miR-125b is a conserved microRNA that is enriched
in the nervous system. We have previously reported the function of
miR-125b in neuronal differentiation of human cell lines. We also
discovered the function of miR-125b in regulating p53 in human and
zebrafish. Here we further characterize the brain defects in zebrafish
embryos injected with morpholinos against miR-125b. Our data
confirm the essential role of miR-125b in brain morphogenesis
particularly in maintaining the balance between proliferation, cell
death and differentiation. We identified lunatic fringe (lfng) as an
additional target of miR-125b in human and zebrafish and suggest
that lfng may mediate the function of miR-125b in neurogenesis.
Together, this report reveals new insights into the function of miR-
125b during neural development of zebrafish.
Abstract: From the perspective of system of systems (SoS) and
emergent behaviors, this paper describes large scale application
software systems, and proposes framework methods to further depict
systems- functional and non-functional characteristics. Besides, this
paper also specifically discusses some functional frameworks. In the
end, the framework-s applications in system disintegrations, system
architecture and stable intermediate forms are additionally dealt with
in this in building, deployment and maintenance of large scale
software applications.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: Seasonal variability of nutrients concentration in the Baltic Sea using the 3D ecosystem numerical model 3D-CEMBS has been investigated. Additionally this study shows horizontal and vertical distribution of nutrients in the Baltic Sea. Model domain is an extended Baltic Sea area divided into 600x640 horizontal grid cells. Aside from standard hydrodynamic parameters 3D-CEMBS produces modeled ecological variables such as: three types of phytoplankton, two detrital classes, dissolved oxygen and the nutrients (nitrate, ammonium, phosphate and silicate). The presented model allows prediction of parameters that describe distribution of nutrients concentration and phytoplankton biomass. 3D-CEMBS can be used to study the effect of different hydrodynamic and biogeochemical processes on distributions of these variables in a larger scale.
Abstract: In this research, the researchers have managed to
design a model to investigate the current trend of stock price of the
"IRAN KHODRO corporation" at Tehran Stock Exchange by
utilizing an Adaptive Neuro - Fuzzy Inference system. For the Longterm
Period, a Neuro-Fuzzy with two Triangular membership
functions and four independent Variables including trade volume,
Dividend Per Share (DPS), Price to Earning Ratio (P/E), and also
closing Price and Stock Price fluctuation as an dependent variable are
selected as an optimal model. For the short-term Period, a neureo –
fuzzy model with two triangular membership functions for the first
quarter of a year, two trapezoidal membership functions for the
Second quarter of a year, two Gaussian combination membership
functions for the third quarter of a year and two trapezoidal
membership functions for the fourth quarter of a year were selected
as an optimal model for the stock price forecasting. In addition, three
independent variables including trade volume, price to earning ratio,
closing Stock Price and a dependent variable of stock price
fluctuation were selected as an optimal model. The findings of the
research demonstrate that the trend of stock price could be forecasted
with the lower level of error.
Abstract: The aim of this study was to compare the effects
of an altitude training camp on heart rate variability and
performance in elite triathletes. Ten athletes completed 20 days of live-high, train-low training at 1650m. Athletes
underwent pre and post 800-m swim time trials at sea-level, and two heart rate variability tests at 1650m on the first and
last day of the training camp. Based on their time trial results,
athletes were divided into responders and non-responders. Relative to the non-responders, the responders sympathetic-toparasympathetic
ratio decreased substantially after 20 days of altitude training (-0.68 ± 1.08 and -1.2 ± 0.96, mean ± 90%
confidence interval for supine and standing respectively). In
addition, sympathetic activity while standing was also
substantially lower post-altitude in the responders compared to the non-responders (-1869 ± 4764 ms2). Results indicate that
responders demonstrated a change to more vagal
predominance compared to non-responders.
Abstract: The purpose of this study is comparing and analysing
of the financial characteristics for development methods of the urban development project in the established area, focusing on the
multi-level replotting.
Analysis showed that the type of the lowest expenditure was
'combination type of group-land and multi-level replotting' and the type of the highest profitability was 'multi-level replotting type'. But
'multi-level replotting type' has still risk of amount of cost for the additional architecture. In addition, we subdivided standard amount
for liquidation of replotting and analysed income-expenditure flow.
Analysis showed that both of 'multi-level replotting type' and 'combination type of group-land and multi-level replotting' improved
profitability of project and property change ratio. However, when the
standard was under a certain amount, amount of original property for the replotting was increased exponentially, and profitability of project.
Abstract: Biclustering aims at identifying several biclusters that
reveal potential local patterns from a microarray matrix. A bicluster is
a sub-matrix of the microarray consisting of only a subset of genes
co-regulates in a subset of conditions. In this study, we extend the
motif of subspace clustering to present a K-biclusters clustering (KBC)
algorithm for the microarray biclustering issue. Besides minimizing
the dissimilarities between genes and bicluster centers within all
biclusters, the objective function of the KBC algorithm additionally
takes into account how to minimize the residues within all biclusters
based on the mean square residue model. In addition, the objective
function also maximizes the entropy of conditions to stimulate more
conditions to contribute the identification of biclusters. The KBC
algorithm adopts the K-means type clustering process to efficiently
make the partition of K biclusters be optimized. A set of experiments
on a practical microarray dataset are demonstrated to show the
performance of the proposed KBC algorithm.
Abstract: Saturated two-phase fluid flows are often subject to
pressure induced oscillations. Due to compressibility the vapor
bubbles act as a spring with an asymmetric non-linear characteristic.
The volume of the vapor bubbles increases or decreases differently if
the pressure fluctuations are compressing or expanding;
consequently, compressing pressure fluctuations in a two-phase pipe
flow cause less displacement in the direction of the pipe flow than
expanding pressure fluctuations. The displacement depends on the
ratio of liquid to vapor, the ratio of pressure fluctuations over average
pressure and on the exciting frequency of the pressure fluctuations.
In addition, pressure fluctuations in saturated vapor bubbles cause
condensation and evaporation within the bubbles and change
periodically the ratio between liquid to vapor, and influence the
dynamical parameters for the oscillation. The oscillations are
conforming to an isenthalpic process at constant enthalpy with no
heat transfer and no exchange of work.
The paper describes the governing non-linear equation for twophase
fluid oscillations with condensation and evaporation, and
presents steady state approximate solutions for free and for pressure
induced oscillations. Resonance criteria and stability are discussed.
Abstract: We propose an enhanced collaborative filtering
method using Hofstede-s cultural dimensions, calculated for 111
countries. We employ 4 of these dimensions, which are correlated to
the costumers- buying behavior, in order to detect users- preferences
for items. In addition, several advantages of this method
demonstrated for data sparseness and cold-start users, which are
important challenges in collaborative filtering. We present
experiments using a real dataset, Book Crossing Dataset.
Experimental results shows that the proposed algorithm provide
significant advantages in terms of improving recommendation
quality.
Abstract: This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
Abstract: Typhoon Morakot hit Taiwan in 2009 and caused
severe damages. The government employs a compulsory relocation
strategy for post-disaster reconstruction. This study analyzes the
impact of this strategy on community solidarity. It employs a multiple
approach for data collection, including semi-structural interview,
secondary data, and documentation. The results indicate that the
government-s strategy for distributing housing has led to conflicts
within the communities. In addition, the relocating process has
stimulated tensions between victims of the disaster and those residents
whose lands were chosen to be new sites for relocation. The
government-s strategy of “collective relocation" also worsened
community integration. In addition, the fact that a permanent housing
community may accommodate people from different places also posts
challenge for the development of new inter-personal relations in the
communities. This study concludes by emphasizing the importance of
bringing social, economic and cultural aspects into consideration for
post-disaster relocation..
Abstract: In this work, we study the impact of dynamically
changing link slowdowns on the stability properties of packetswitched
networks under the Adversarial Queueing Theory
framework. Especially, we consider the Adversarial, Quasi-Static
Slowdown Queueing Theory model, where each link slowdown may
take on values in the two-valued set of integers {1, D} with D > 1
which remain fixed for a long time, under a (w, ¤ü)-adversary. In this
framework, we present an innovative systematic construction for the
estimation of adversarial injection rate lower bounds, which, if
exceeded, cause instability in networks that use the LIS (Longest-in-
System) protocol for contention-resolution. In addition, we show that
a network that uses the LIS protocol for contention-resolution may
result in dropping its instability bound at injection rates ¤ü > 0 when
the network size and the high slowdown D take large values. This is
the best ever known instability lower bound for LIS networks.
Abstract: The paper reports on the results of experimental and
numerical study of nonstationary swirling flow in an isothermal
model of vortex burner. It has been identified that main source of the
instability is related to a precessing vortex core (PVC) phenomenon.
The PVC induced flow pulsation characteristics such as precession
frequency and its variation as a function of flowrate and swirl number
have been explored making use of acoustic probes. Additionally
pressure transducers were used to measure the pressure drops on the
working chamber and across the vortex flow. The experiments have
been included also the mean velocity measurements making use of a
laser-Doppler anemometry. The features of instantaneous flowfield
generated by the PVC were analyzed employing a commercial CFD
code (Star-CCM+) based on Detached Eddy Simulation (DES)
approach. Validity of the numerical code has been checked by
comparison calculated flowfield data with the obtained experimental
results. It has been confirmed particularly that the CFD code applied
correctly reproduces the flow features.
Abstract: In the recent years, high dynamic range imaging has
gain popularity with the advancement in digital photography. In this
contribution we present a subjective evaluation of various tone
production and tone mapping techniques by a number of participants.
Firstly, standard HDR images were used and the participants were
asked to rate them based on a given rating scheme. After that, the
participant was asked to rate HDR image generated using linear and
nonlinear combination approach of multiple exposure images. The
experimental results showed that linearly generated HDR images
have better visualization than the nonlinear combined ones. In
addition, Reinhard et al. and the exponential tone mapping operators
have shown better results compared to logarithmic and the Garrett et
al. tone mapping operators.
Abstract: Vehicular Ad-Hoc Networks (VANET) can provide
communications between vehicles or infrastructures. It provides the
convenience of driving and the secure driving to reduce accidents. In
VANET, the security is more important because it is closely related to
accidents. Additionally, VANET raises a privacy issue because it can
track the location of vehicles and users- identity when a security
mechanism is provided. In this paper, we analyze the problem of an
existing solution for security requirements required in VANET, and
resolve the problem of the existing method when a key management
mechanism is provided for the security operation in VANET.
Therefore, we show suitability of the Long Term Evolution (LTE) in
VANET for the solution of this problem.
Abstract: The aim of the present paper is to investigate the
interdependency among ego-identity status, autobiographical memory
and cultural life story schema. The study shows considerable
differences between autobiographical memory characteristics and
“family script", which is typical for participants (adolescents, M age
years = 17.84, SD = 1.18, N = 58), with different ego-identity
statuses. Participants with diffused ego-identity status recalled fewer
autobiographical memories. Additionally, this group of participants
recalled fewer events from their parents- life. Participants with
moratorium ego-identity status dated their first recollections to a later
age than others, and recalled fewer memories relating to their
childhood. Participants with achieved identity status recalled more
self-defining memories and events from their parents- life. They used
more functions from the autobiographical memory. There weren-t
any significant differences between the foreclosed identity status
group and the others. These findings support the idea of a
bidirectional relation between culture, memory and self.
Abstract: The aim of this qualitative case study is to examine how school principals perform their new roles and responsibilities defined in accordance with the new curriculum. Of ten primary schools that the new curriculum was piloted in Istanbul in school year of 2004-2005, one school was randomly selected as the sample of the study. The participants of the study were comprised of randomly-selected 26 teachers working in the case school. To collect data, an interview schedule was developed based on the new role definitions for school principals by the National Ministry of Education. Participants were interviewed on one-to-one basis in February and March 2007. Overall results showed that the school principal was perceived to be successful in terms of the application of the new curriculum in school. According to the majority of teachers, the principal has done his best to establish the infrastructure that is necessary for successful application of the new program. In addition to these, the principal was reported to adopt a collegial and participatory leadership style by creating a positive school atmosphere that enables the school community (teachers, parents and students) to involve school more than before. Keywordscase study, curriculum implementation, school principals and curriculum
Abstract: An emotional speech recognition system for the
applications on smart phones was proposed in this study to combine
with 3G mobile communications and social networks to provide users
and their groups with more interaction and care. This study developed
a mechanism using the support vector machines (SVM) to recognize
the emotions of speech such as happiness, anger, sadness and normal.
The mechanism uses a hierarchical classifier to adjust the weights of
acoustic features and divides various parameters into the categories of
energy and frequency for training. In this study, 28 commonly used
acoustic features including pitch and volume were proposed for
training. In addition, a time-frequency parameter obtained by
continuous wavelet transforms was also used to identify the accent and
intonation in a sentence during the recognition process. The Berlin
Database of Emotional Speech was used by dividing the speech into
male and female data sets for training. According to the experimental
results, the accuracies of male and female test sets were increased by
4.6% and 5.2% respectively after using the time-frequency parameter
for classifying happy and angry emotions. For the classification of all
emotions, the average accuracy, including male and female data, was
63.5% for the test set and 90.9% for the whole data set.