Abstract: The role of corporate governance is to reduce the
divergence of interests between shareholders and managers. The role
of corporate governance is more useful when managers have an
incentive to deviate from shareholders- interests. One example of
management-s deviation from shareholders- interests is the
management of earnings through the use of accounting accruals. This
paper examines the association between corporate governance
internal mechanisms ownership concentration, board independence,
the existence of CEO-Chairman duality and earnings management.
Firm size and leverage are control variables. The population used in
this study comprises firms listed on the Tehran Stock Exchange
(TSE) between 2004 and 2008, the sample comprises 196 firms.
Panel Data method is employed as technique to estimate the model.
We find that there is negative significant association between
ownership concentration and board independence manage earnings
with earnings management, there is negative significant association
between the existence of CEO-Chairman duality and earnings
management. This study also found a positive significant association
between control variable (firm size and leverage) and earnings
management.
Abstract: The two agro-ecological zones became the focus of
the study because of violent nature of the incessant conflict in the
zones. The available register of farmers association was the sampling
frame work where ten percent (61) farmers per state were randomly
sampled. Data were collected and analysed using z-test. The research
findings revealed tree crops and grains production enterprises ranked
higher in Osun (rain fed zones) and Taraba states (savannah zones)
respectively. Osun state entrepreneur felt the effect of the conflict on
their enterprises more than Tarba state. The reasons adduced for
severity of the conflict on enterprises are majority (77.0%) migrated
and (75.5%) of them were not allowed to enter their farms during and
when conflict deescalated unlike situation in Taraba state. The
different in enterprises production level between the two agroecological
zone was statistically significant at p
Abstract: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
Abstract: Calcium [Ca2+] dynamics is studied as a potential form
of neuron excitability that can control many irregular processes like
metabolism, secretion etc. Ca2+ ion enters presynaptic terminal and
increases the synaptic strength and thus triggers the neurotransmitter
release. The modeling and analysis of calcium dynamics in neuron
cell becomes necessary for deeper understanding of the processes
involved. A mathematical model has been developed for cylindrical
shaped neuron cell by incorporating physiological parameters like
buffer, diffusion coefficient, and association rate. Appropriate initial
and boundary conditions have been framed. The closed form solution
has been developed in terms of modified Bessel function. A computer
program has been developed in MATLAB 7.11 for the whole
approach.
Abstract: In social network analysis the mean nodal degree and
density of the graph can be considered as a measure of the activity of
all actors in the network and this is an important property of a graph
and for making comparisons among networks. Since subjects in a
family or organization are subject to common environment factors, it
is prime interest to study the association between responses.
Therefore, we study the distribution of the mean nodal degree and
density of the graph under correlated binary units. The cross product
ratio is used to capture the intra-units association among subjects.
Computer program and an application are given to show the benefits
of the method.
Abstract: Mutations of the telomeric copy of the survival motor neuron 1 (SMN1) gene cause spinal muscular atrophy. A deletion of the Eef1a2 gene leads to lower motor neuron degeneration in wasted mice. Indirect evidences have been shown that the eEF1A protein family may interact with SMN, and our previous study showed that abnormalities of neuromuscular junctions in wasted mice were similar to those of Smn mutant mice. To determine potential colocalization between SMN and tissue-specific translation elongation factor 1A2 (eEF1A2), an immunochemical analysis of HeLa cells transfected with the plasmid pcDNA3.1(+)C-hEEF1A2- myc and a new quantitative test of colocalization by intensity correlation analysis (ICA) was used to explore the association of SMN and eEF1A2. Here the results showed that eEF1A2 redistributed from the cytoplasm to the nucleus in response to serum and epidermal growth factor. In the cytoplasm, compelling evidence showed that staining for myc-tagged eEF1A2 varied in synchrony with that for SMN, consistent with the formation of a SMN-eEF1A2 complex in the cytoplasm of HeLa cells. These findings suggest that eEF1A2 may colocalize with SMN in the cytoplasm and may be a component of the SMN complex. However, the limitation of the ICA method is an inability to resolve colocalization in components of small organelles such as the nucleus.
Abstract: The problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items between transactions computed instead of creating itemset and computing their frequency. With applying real life transactions and some consumption is taken from real life data, the significant efficiency acquire from databases in generation association rules mining.
Abstract: In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.
Abstract: Even though most researchers would agree that in
symbiotic relationships, like the one between parent and child,
influences become reciprocal over time, empirical evidence
supporting this claim is limited. The aim of the current study was to
develop and test a model describing the reciprocal influence between
characteristics of the parent-child relationship, such as closeness and
conflict, and the child-s bullying and victimization experiences at
school. The study used data from the longitudinal Study of Early
Child-Care, conducted by the National Institute of Child Health and
Human Development. The participants were dyads of early
adolescents (5th and 6th graders during the two data collection waves)
and their mothers (N=1364). Supporting our hypothesis, the findings
suggested a reciprocal association between bullying and positive
parenting, although this association was only significant for boys.
Victimization and positive parenting were not significantly
interrelated.
Abstract: The problem of frequent pattern discovery is defined
as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns
has become an important data mining task because it reveals associations, correlations, and many other interesting relationships
hidden in a database. Most of the proposed frequent pattern mining
algorithms have been implemented with imperative programming
languages. Such paradigm is inefficient when set of patterns is large
and the frequent pattern is long. We suggest a high-level declarative
style of programming apply to the problem of frequent pattern
discovery. We consider two languages: Haskell and Prolog. Our
intuitive idea is that the problem of finding frequent patterns should
be efficiently and concisely implemented via a declarative paradigm
since pattern matching is a fundamental feature supported by most
functional languages and Prolog. Our frequent pattern mining
implementation using the Haskell and Prolog languages confirms our
hypothesis about conciseness of the program. The comparative
performance studies on line-of-code, speed and memory usage of
declarative versus imperative programming have been reported in the
paper.
Abstract: One astonishing capability of humans is to recognize thousands of different objects visually, and to learn the semantic association between those objects and words referring to them. This work is an attempt to build a computational model of such capacity,simulating the process by which infants learn how to recognize objects and words through exposure to visual stimuli and vocal sounds.One of the main fact shaping the brain of a newborn is that lights and colors come from entities of the world. Gradually the visual system learn which light sensations belong to same entities, despite large changes in appearance. This experience is common between humans and several other mammals, like non-human primates. But humans only can recognize a huge variety of objects, most manufactured by himself, and make use of sounds to identify and categorize them. The aim of this model is to reproduce these processes in a biologically plausible way, by reconstructing the essential hierarchy of cortical circuits on the visual and auditory neural paths.
Abstract: Safety Health and Environment Code of Practice (SHE
COP) was developed to help road transportation operators to manage
its operation in a systematic and safe manner. A study was conducted
to determine the effectiveness of SHE COP implementation during
non-OPS period. The objective of the study is to evaluate the
implementations of SHE COP among bus operators during wee hour
operations. The data was collected by completing a set of checklist
after observing the activities during pre departure, during the trip, and
upon arrival. The results show that there are seven widely practiced
SHE COP elements. 22% of the buses have average speed exceeding
the maximum permissible speed on the highways (90 km/h), with
13% of the buses were travelling at the speed of more than 100 km/h.
The statistical analysis shows that there is only one significant
association which relates speeding with prior presence of
enforcement officers.
Abstract: Modes of occurrence of Pb, As, Cr, Co, Cu, and Ni in bituminous coal and lignite were determined by means of sequential extraction using NH4OAc, HCl, HF and HNO3 extraction solutions. Elemental affinities obtained were then evaluated in relation to volatility of these elements during the combustion of these coals in two circulating fluidised-bed power stations. It was found out that higher percentage of the elements bound in silicates brought about lower volatility, while higher elemental proportion with monosulphides association (or bound as exchangeable ion) resulted in higher volatility. The only exception was the behavior of arsenic, whose volatility depended on amount of limestone added during the combustion process (as desulphurisation additive) rather than to its association in coal.
Abstract: In data mining, the association rules are used to find
for the associations between the different items of the transactions
database. As the data collected and stored, rules of value can be found
through association rules, which can be applied to help managers
execute marketing strategies and establish sound market frameworks.
This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth)
to derive from fuzzy association rules. At first, we apply fuzzy
partition methods and decide a membership function of quantitative
value for each transaction item. Next, we implement FFP-growth
to deal with the process of data mining. In addition, in order to
understand the impact of Apriori algorithm and FFP-growth algorithm
on the execution time and the number of generated association
rules, the experiment will be performed by using different sizes of
databases and thresholds. Lastly, the experiment results show FFPgrowth
algorithm is more efficient than other existing methods.
Abstract: In Virtual organization, Knowledge Discovery (KD)
service contains distributed data resources and computing grid nodes.
Computational grid is integrated with data grid to form Knowledge
Grid, which implements Apriori algorithm for mining association
rule on grid network. This paper describes development of parallel
and distributed version of Apriori algorithm on Globus Toolkit using
Message Passing Interface extended with Grid Services (MPICHG2).
The creation of Knowledge Grid on top of data and
computational grid is to support decision making in real time
applications. In this paper, the case study describes design and
implementation of local and global mining of frequent item sets. The
experiments were conducted on different configurations of grid
network and computation time was recorded for each operation. We
analyzed our result with various grid configurations and it shows
speedup of computation time is almost superlinear.
Abstract: The main thrust of this paper is to assess the level of disclosure in the annual reports of non-financial Greek firms and to empirically investigate the hypothesized impact of several firm characteristics on the extent of mandatory disclosure. A disclosure checklist consisting of 100 mandatory items was developed to assess the level of disclosure in the 2009 annual reports of 43 Greek companies listed at the Athens stock exchange. The association between the level of disclosure and some firm characteristics was examined using multiple linear regression analysis. The study reveals that Greek companies on general have responded adequately to the mandatory disclosure requirements of the regulatory bodies. The findings also indicate that firm size was significant positively associated with the level of disclosure. The remaining variables such as age, profitability, liquidity, and board composition were found to be insignificant in explaining the variation of mandatory disclosures. The outcome of this study is undoubtedly of great concern to the investment community at large to assist in evaluating the extent of mandatory disclosure by Greek firms and explaining the variation of disclosure in light of firm-specific characteristics.
Abstract: Data mining techniques have been used in medical
research for many years and have been known to be effective. In order
to solve such problems as long-waiting time, congestion, and delayed
patient care, faced by emergency departments, this study concentrates
on building a hybrid methodology, combining data mining techniques
such as association rules and classification trees. The methodology is
applied to real-world emergency data collected from a hospital and is
evaluated by comparing with other techniques. The methodology is
expected to help physicians to make a faster and more accurate
classification of chest pain diseases.
Abstract: A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.
Abstract: A generic and extendible Multi-Agent Data Mining
(MADM) framework, MADMF (the Multi-Agent Data Mining
Framework) is described. The central feature of the framework is that
it avoids the use of agreed meta-language formats by supporting a
framework of wrappers.
The advantage offered is that the framework is easily extendible,
so that further data agents and mining agents can simply be added to
the framework. A demonstration MADMF framework is currently
available. The paper includes details of the MADMF architecture and
the wrapper principle incorporated into it. A full description and
evaluation of the framework-s operation is provided by considering
two MADM scenarios.
Abstract: This article illustrates that how non similar culture become a cause of constant anxiety among international students in China. For that, a survey was carried out among international students of Wuhan University, China. The association among non similar culture, non familiarity of Chinese culture, self finance students and food problem is looked at through a regression line, and in the light of empirical results, a model is anticipated which elucidates these results. Some suggestions were directed at the end which will help to mitigate the anxiety among prospective students in Chinese universities.