Abstract: The relation between taxation states and foreign direct
investment has been studied for several perspectives and with states
of different levels of development. Usually it's only considered the
impact of tax level on the foreign direct investment volume. This
paper enhances this view by assuming that multinationals companies
(MNC) can use transfer prices systems and have got investment
timing flexibility. Thus, it evaluates the impact of the use of
international transfer pricing systems on the states- policy and on the
investment timing of the multinational companies. In uncertain
business environments (with periodical release of news), the
investment can increase if MNC detain investment delay options.
This paper shows how tax differentials can attract foreign direct
investments (FDI) and influence MNC behavior. The equilibrium is
set in a global environment where MNC can shift their profits
between states depending on the corporate tax rates. Assuming the
use of transfer pricing schemes, this paper confirms the relationship
between MNC behavior and the release of new business news.
Abstract: We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.
Abstract: The given work is devoted to the description of
Information Technologies NAS of Azerbaijan created and
successfully maintained in Institute. On the basis of the decision of
board of the Supreme Certifying commission at the President of the
Azerbaijan Republic and Presidium of National Academy of
Sciences of the Azerbaijan Republic, the organization of training
courses on Computer Sciences for all post-graduate students and
dissertators of the republic, taking of examinations of candidate
minima, it was on-line entrusted to Institute of Information
Technologies of the National Academy of Sciences of Azerbaijan.
Therefore, teaching the computer sciences to post-graduate
students and dissertators a scientific - methodological manual on
effective application of new information technologies for research
works by post-graduate students and dissertators and taking of
candidate minima is carried out in the Educational Center.
Information and communication technologies offer new
opportunities and prospects of their application for teaching and
training. The new level of literacy demands creation of essentially
new technology of obtaining of scientific knowledge. Methods of
training and development, social and professional requirements,
globalization of the communicative economic and political projects
connected with construction of a new society, depends on a level of
application of information and communication technologies in the
educational process. Computer technologies develop ideas of
programmed training, open completely new, not investigated
technological ways of training connected to unique opportunities of
modern computers and telecommunications. Computer technologies
of training are processes of preparation and transfer of the
information to the trainee by means of computer. Scientific and
technical progress as well as global spread of the technologies
created in the most developed countries of the world is the main
proof of the leading role of education in XXI century. Information
society needs individuals having modern knowledge. In practice, all
technologies, using special technical information means (computer,
audio, video) are called information technologies of education.
Abstract: This study examined the effects of 8-week Pilates training program on limits of stability (LOS) and abdominal muscle strength in young dancers. Twenty-four female volunteered and randomly assigned as experimental group (EG) or control group (CG). All subjects received the same dance lessons but the EG underwent an extra Pilates mat exercises for 40 minutes, three times a week, for 8 weeks. LOS was evaluated by the Biodex Balance System and the abdominal strength was measured by 30/60 seconds sit-ups test. One factor ANCOVA was used to examine the differences between groups after training. The results showed that the overall LOS scores at levels 2/8 and the 30/60 seconds sit-ups for the EG group pre- and post-training were changed from 22/38 % to 31/51 % and 20/33 times to 24/42 times, respectively. The study demonstrated that 8-week Pilates training can improve the LOS performance and abdominal strength in young dancers.
Abstract: Construction project control attempts to obtain real-time information and effectively enhance dynamic control and management via information sharing and analysis among project participants to eliminate construction conflicts and project delays. However, survey results for Taiwan indicate that construction commercial project management software is not widely accepted for subcontractors and suppliers. To solve the project communications problems among participants, this study presents a novel system called the Construction Dynamic Teams Communication Management (Con-DTCM) system for small-to-medium sized subcontractors and suppliers in Taiwanese Construction industry, and demonstrates that the Con-DTCM system responds to the most recent project information efficiently and enhances management of project teams (general contractor, suppliers and subcontractors) through web-based environment. Web-based technology effectively enhances information sharing during construction project management, and generates cost savings via the Internet. The main unique characteristic of the proposed Con-DTCM system is extremely user friendly and easily design compared with current commercial project management applications. The Con-DTCM system is applied to a case study of construction of a building project in Taiwan to confirm the proposed methodology and demonstrate the effectiveness of information sharing during the construction phase. The advantages of the Con-DTCM system are in improving project control and management efficiency for general contractors, and in providing dynamic project tracking and management, which enables subcontractors and suppliers to acquire the most recent project-related information. Furthermore, this study presents and implements a generic system architecture.
Abstract: Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.
Abstract: The main issue of interest here is whether individuals
who differ in arithmetical reasoning ability and levels of imagery ability display different brain activity during the conduct of mental
arithmetical reasoning tasks. This was a case study of four
participants who represented four extreme combinations of Maths –Imagery abilities: ie., low-low, high-high, high-low, low-high respectively. As the Ps performed a series of 60 arithmetical reasoning tasks, 128-channel EEG recordings were taken and the
pre-response interval subsequently analysed using EGI GeosourceTM
software. The P who was high in both imagery and maths ability
showed peak activity prior to response in BA7 (superior parietal cortex) but other Ps did not show peak activity in this region. The
results are considered in terms of the diverse routes that may be employed by individuals during the conduct of arithmetical reasoning
tasks and the possible implications of this for mathematics education.
Abstract: This paper proposes a new technique based on nonlinear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modeled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local neighborhood. It appears as a sprinkle of both light and dark or only light spots in the image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly efficient but for large window and in case of high noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM) filter has got a better average performance over the median filter. However the original pixel corrupted and noise reduction is substantial under high noise condition. Hence this technique has also blurring affect on the image. To illustrate the superiority of the proposed approach, the proposed new scheme has been simulated along with the standard ones and various restored performance measures have been compared.
Abstract: This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.
Abstract: In this paper, two versions of an iterative loopless
algorithm for the classical towers of Hanoi problem with O(1) storage complexity and O(2n) time complexity are presented. Based
on this algorithm the number of different moves in each of pegs with its direction is formulated.
Abstract: This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.
Abstract: Random and natural textures classification is still
one of the biggest challenges in the field of image processing and
pattern recognition. In this paper, texture feature extraction using
Slant Hadamard Transform was studied and compared to other
signal processing-based texture classification schemes. A
parametric SHT was also introduced and employed for natural
textures feature extraction. We showed that a subtly modified
parametric SHT can outperform ordinary Walsh-Hadamard
transform and discrete cosine transform. Experiments were carried
out on a subset of Vistex random natural texture images using a
kNN classifier.
Abstract: Broccoli has been widely recognized as a wealthy
vegetable which contains multiple nutrients with potent anti-cancer
properties. Lamb’s lettuce has been used as food for many centuries
but only recently became commercially available and literature is
therefore exiguous concerning these vegetables. The aim of this work
was to evaluate the influence of the extraction conditions on the yield
of phenolic compounds and the corresponding antioxidant capacity of
broccoli and lamb’s lettuce. The results indicate that lamb’s lettuce,
compared to broccoli, contains simultaneously a large amount of total
polyphenols as well as high antioxidant activity. It is clearly
demonstrated that extraction solvent significantly influences the
antioxidant activity. Methanol is the solvent that can globally
maximize the antioxidant extraction yield. The results presented
herein prove lamb’s lettuce as a very interesting source of
polyphenols, and thus a potential health-promoting food.
Abstract: With the proliferation of the mobile device
technologies, mobile learning can be used to complement and
improve traditional learning problems. Both students and teachers
need a proper and handy system to monitor and keep track the
performance of the students. This paper presents an implementation
of M-learning for primary school in Malaysia by using an open
source technology. It focuses on learning mathematics using
handheld devices for primary schools- students aged 11 and 12 years
old. Main users for this system include students, teachers and the
administrator. This application suggests a new mobile learning
environment with mobile graph for tracking the students- progress
and performance. The purpose of this system is not to replace
traditional classroom but to complement the learning process. In a
testing conducted, students who used this system performed better in
their examination.
Abstract: To determine if the murine insulinoma, β-TC-6, is a
suitable substitute for primary pancreatic β-cells in the study of β-
cell functional heterogeneity, we used three distinct functional assays
to ascertain the cell line-s response to glucose or a glucose analog.
These assays include: (i) a 2-NBDG uptake assay; (ii) a calcium
influx assay, and; (iii) a quinacrine secretion assay. We show that a
population of β-TC-6 cells endocytoses the glucose analog, 2-
NBDG, at different rates, has non-uniform intracellular calcium ion
concentrations and releases quinacrine at different rates when
challenged with glucose. We also measured the Km for β-TC-6
glucose uptake to be 46.9 mM and the Vm to be 8.36 x 10-5
mmole/million cells/min. These data suggest that β-TC-6 might be
used as an alternative to primary pancreatic β-cells for the study of
glucose-dependent β-cell functional heterogeneity.
Abstract: For scores of years now, several microfinance
organizations, non governmental organizations and other welfare
organizations have, with a view to aiding the progress of
communities rooted in poverty have been focusing on creating
microentrepreneurs, besides taking several other measures. In recent
times, business corporations have joined forces to combat poverty by
taking up microenterprise development. Hindustan Unilever Limited
(HUL), the Indian subsidiary of Unilever Limited exemplifies this
through its Project Shakti. The company through the Project creates
rural women entrepreneurs by making them direct to home sales
distributors of its products in villages that have thus far been ignored
by multinational corporations. The members participating in Project
Shakti are largely self help group members. The paper focuses on
assessing the impact made by the company on the members engaged
in Project Shakti. The analysis involves use of quantitative methods
to study the effect of Project Shakti on those self help group
members engaged in Project Shakti and those not engaged with
Project Shakti. Path analysis has been used to study the impact made
on those members engaged in Project Shakti. Significant differences
were observed on fronts of entrepreneurial development, economic
empowerment and social empowerment between members associated
with Project Shakti and those not associated with Project Shakti.
Path analysis demonstrated that involvement in Project Shakti led to
entrepreneurial development resulting in economic empowerment
that in turn led to social empowerment and that these three elements
independently induced a feeling of privilege in the women for being
associated with the Project.
Abstract: Signature represents an individual characteristic of a
person which can be used for his / her validation. For such application
proper modeling is essential. Here we propose an offline signature
recognition and verification scheme which is based on extraction of
several features including one hybrid set from the input signature
and compare them with the already trained forms. Feature points
are classified using statistical parameters like mean and variance.
The scanned signature is normalized in slant using a very simple
algorithm with an intention to make the system robust which is
found to be very helpful. The slant correction is further aided by the
use of an Artificial Neural Network (ANN). The suggested scheme
discriminates between originals and forged signatures from simple
and random forgeries. The primary objective is to reduce the two
crucial parameters-False Acceptance Rate (FAR) and False Rejection
Rate (FRR) with lesser training time with an intension to make the
system dynamic using a cluster of ANNs forming a multiple classifier
system.
Abstract: Hidden failure in a protection system has been
recognized as one of the main reasons which may cause to a power
system instability leading to a system cascading collapse. This paper
presents a computationally systematic approach used to obtain the
estimated average probability of a system cascading collapse by
considering the effect of probability hidden failure in a protection
system. The estimated average probability of a system cascading
collapse is then used to determine the severe loading condition
contributing to the higher risk of critical system cascading collapse.
This information is essential to the system utility since it will assist
the operator to determine the highest point of increased system
loading condition prior to the event of critical system cascading
collapse.
Abstract: Collision is considered as a time-depended nonlinear
dynamic phenomenon. The majority of researchers have focused on
deriving the resultant damage of the ship collisions via analytical,
experimental, and finite element methods.In this paper, first, the
force-penetration curve of a head collision on a container ship with
rigid barrier based on Yang and Pedersen-s methods for internal
mechanic section is studied. Next, the obtained results from different
analytical methods are compared with each others. Then, through a
simulation of the container ship collision in Ansys Ls-Dyna, results
from finite element approach are compared with analytical methods
and the source of errors is discussed. Finally, the effects of
parameters such as velocity, and angle of collision on the forcepenetration
curve are investigated.
Abstract: In this paper, we study the multi-scenario knapsack problem, a variant of the well-known NP-Hard single knapsack problem. We investigate the use of an adaptive algorithm for solving heuristically the problem. The used method combines two complementary phases: a size reduction phase and a dynamic 2- opt procedure one. First, the reduction phase applies a polynomial reduction strategy; that is used for reducing the size problem. Second, the adaptive search procedure is applied in order to attain a feasible solution Finally, the performances of two versions of the proposed algorithm are evaluated on a set of randomly generated instances.