Abstract: New Growth Theory helps us make sense of the
ongoing shift from a resource-based economy to a knowledge-based
economy. It underscores the point that the economic processes which
create and diffuse new knowledge are critical to shaping the growth
of nations, communities and individual firms. In all too many
contributions to New (Endogenous) Growth Theory – though not in
all – central reference is made to 'a stock of knowledge', a 'stock of
ideas', etc., this variable featuring centre-stage in the analysis. Yet it
is immediately apparent that this is far from being a crystal clear
concept. The difficulty and uncertainty of being able to capture the
value associated with knowledge is a real problem. The intent of this
paper is introducing new thinking and theorizing about the
knowledge and its measurability in new growth theory. Moreover the
study aims to synthesize various strain of the literature with a
practical bearing on knowledge concept. By contribution of
institution framework which is found within NGT, we can indirectly
measure the knowledge concept. Institutions matter because they
shape the environment for production and employment of new
knowledge
Abstract: Hazardous Material transportation by road is coupled
with inherent risk of accidents causing loss of lives, grievous injuries,
property losses and environmental damages. The most common type
of hazmat road accident happens to be the releases (78%) of
hazardous substances, followed by fires (28%), explosions (14%) and
vapour/ gas clouds (6 %.).
The paper is discussing initially the probable 'Impact Zones'
likely to be caused by one flammable (LPG) and one toxic (ethylene
oxide) chemicals being transported through a sizable segment of a
State Highway connecting three notified Industrial zones in Surat
district in Western India housing 26 MAH industrial units. Three
'hotspots' were identified along the highway segment depending on
the particular chemical traffic and the population distribution within
500 meters on either sides. The thermal radiation and explosion
overpressure have been calculated for LPG / Ethylene Oxide BLEVE
scenarios along with toxic release scenario for ethylene oxide.
Besides, the dispersion calculations for ethylene oxide toxic release
have been made for each 'hotspot' location and the impact zones
have been mapped for the LOC concentrations. Subsequently, the
maximum Initial Isolation and the protective zones were calculated
based on ERPG-3 and ERPG-2 values of ethylene oxide respectively
which are estimated taking the worst case scenario under worst
weather conditions. The data analysis will be helpful to the local
administration in capacity building with respect to rescue /
evacuation and medical preparedness and quantitative inputs to
augment the District Offsite Emergency Plan document.
Abstract: Amount of dissolve oxygen in a river has a great direct affect on aquatic macroinvertebrates and this would influence on the region ecosystem indirectly. In this paper it is tried to predict dissolved oxygen in rivers by employing an easy Fuzzy Logic Modeling, Wang Mendel method. This model just uses previous records to estimate upcoming values. For this purpose daily and hourly records of eight stations in Au Sable watershed in Michigan, United States are employed for 12 years and 50 days period respectively. Calculations indicate that for long period prediction it is better to increase input intervals. But for filling missed data it is advisable to decrease the interval. Increasing partitioning of input and output features influence a little on accuracy but make the model too time consuming. Increment in number of input data also act like number of partitioning. Large amount of train data does not modify accuracy essentially, so, an optimum training length should be selected.
Abstract: Data Envelopment Analysis (DEA) is a methodology
that computes efficiency values for decision making units (DMU) in a
given period by comparing the outputs with the inputs. In many cases,
there are some time lag between the consumption of inputs and the
production of outputs. For a long-term research project, it is hard to
avoid the production lead time phenomenon. This time lag effect
should be considered in evaluating the performance of organizations.
This paper suggests a model to calculate efficiency values for the
performance evaluation problem with time lag. In the experimental
part, the proposed methods are compared with the CCR and an
existing time lag model using the data set of the 21st century frontier
R&D program which is a long-term national R&D program of Korea.
Abstract: Clean air in subway station is important to passengers. The Platform Screen Doors (PSDs) can improve indoor air quality in the subway station; however the air quality in the subway tunnel is degraded. The subway tunnel has high CO2 concentration and indoor particulate matter (PM) value. The Indoor Air Quality (IAQ) level in subway environment degrades by increasing the frequency of the train operation and the number of the train. The ventilation systems of the subway tunnel need improvements to have better air-quality. Numerical analyses might be effective tools to analyze the performance of subway twin-track tunnel ventilation systems. An existing subway twin-track tunnel in the metropolitan Seoul subway system is chosen for the numerical simulations. The ANSYS CFX software is used for unsteady computations of the airflow inside the twin-track tunnel when the train moves. The airflow inside the tunnel is simulated when one train runs and two trains run at the same time in the tunnel. The piston-effect inside the tunnel is analyzed when all shafts function as the natural ventilation shaft. The supplied air through the shafts is mixed with the pollutant air in the tunnel. The pollutant air is exhausted by the mechanical ventilation shafts. The supplied and discharged airs are balanced when only one train runs in the twin-track tunnel. The pollutant air in the tunnel is high when two trains run simultaneously in opposite direction and all shafts functioned as the natural shaft cases when there are no electrical power supplies in the shafts. The remained pollutant air inside the tunnel enters into the station platform when the doors are opened.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: To evaluate genetic variation of wheat (Triticum
aestivum) affected by heat and drought stress on eight Australian
wheat genotypes that are parents of Doubled Haploid (HD) mapping
populations at the vegetative stage, the water stress experiment was
conducted at 65% field capacity in growth room. Heat stress
experiment was conducted in the research field under irrigation over
summer. Result show that water stress decreased dry shoot weight
and RWC but increased osmolarity and means of Fv/Fm values in all
varieties except for Krichauff. Krichauff and Kukri had the
maximum RWC under drought stress. Trident variety was shown
maximum WUE, osmolarity (610 mM/Kg), dry mater, quantum yield
and Fv/Fm 0.815 under water stress condition. However, the
recovery of quantum yield was apparent between 4 to 7 days after
stress in all varieties. Nevertheless, increase in water stress after that
lead to strong decrease in quantum yield. There was a genetic
variation for leaf pigments content among varieties under heat stress.
Heat stress decreased significantly the total chlorophyll content that
measured by SPAD. Krichauff had maximum value of Anthocyanin
content (2.978 A/g FW), chlorophyll a+b (2.001 mg/g FW) and
chlorophyll a (1.502 mg/g FW). Maximum value of chlorophyll b
(0.515 mg/g FW) and Carotenoids (0.234 mg/g FW) content
belonged to Kukri. The quantum yield of all varieties decreased
significantly, when the weather temperature increased from 28 ÔùªC to
36 ÔùªC during the 6 days. However, the recovery of quantum yield
was apparent after 8th day in all varieties. The maximum decrease
and recovery in quantum yield was observed in Krichauff. Drought
and heat tolerant and moderately tolerant wheat genotypes were
included Trident, Krichauff, Kukri and RAC875. Molineux, Berkut
and Excalibur were clustered into most sensitive and moderately
sensitive genotypes. Finally, the results show that there was a
significantly genetic variation among the eight varieties that were
studied under heat and water stress.
Abstract: The purpose of this paper is to perform a multidisciplinary design and analysis (MDA) of honeycomb panels used in the satellites structural design. All the analysis is based on clamped-free boundary conditions. In the present work, detailed finite element models for honeycomb panels are developed and analysed. Experimental tests were carried out on a honeycomb specimen of which the goal is to compare the previous modal analysis made by the finite element method as well as the existing equivalent approaches. The obtained results show a good agreement between the finite element analysis, equivalent and tests results; the difference in the first two frequencies is less than 4% and less than 10% for the third frequency. The results of the equivalent model presented in this analysis are obtained with a good accuracy. Moreover, investigations carried out in this research relate to the honeycomb plate modal analysis under several aspects including the structural geometrical variation by studying the various influences of the dimension parameters on the modal frequency, the variation of core and skin material of the honeycomb. The various results obtained in this paper are promising and show that the geometry parameters and the type of material have an effect on the value of the honeycomb plate modal frequency.
Abstract: A filter is used to remove undesirable frequency information from a dynamic signal. This paper shows that the Znotch filter filtering technique can be applied to remove the noise nuisance from a machining signal. In machining, the noise components were identified from the sound produced by the operation of machine components itself such as hydraulic system, motor, machine environment and etc. By correlating the noise components with the measured machining signal, the interested components of the measured machining signal which was less interfered by the noise, can be extracted. Thus, the filtered signal is more reliable to be analysed in terms of noise content compared to the unfiltered signal. Significantly, the I-kaz method i.e. comprises of three dimensional graphical representation and I-kaz coefficient, Z∞ could differentiate between the filtered and the unfiltered signal. The bigger space of scattering and the higher value of Z∞ demonstrated that the signal was highly interrupted by noise. This method can be utilised as a proactive tool in evaluating the noise content in a signal. The evaluation of noise content is very important as well as the elimination especially for machining operation fault diagnosis purpose. The Z-notch filtering technique was reliable in extracting noise component from the measured machining signal with high efficiency. Even though the measured signal was exposed to high noise disruption, the signal generated from the interaction between cutting tool and work piece still can be acquired. Therefore, the interruption of noise that could change the original signal feature and consequently can deteriorate the useful sensory information can be eliminated.
Abstract: The paper is concerned with relationships between
SSME and ICTs and focuses on the role of Web 2.0 tools in
the service development process. The research presented aims at
exploring how collaborative technologies can support and improve
service processes, highlighting customer centrality and value coproduction.
The core idea of the paper is the centrality of user
participation and the collaborative technologies as enabling factors;
Wikipedia is analyzed as an example. The result of such analysis is
the identification and description of a pattern characterising specific
services in which users collaborate by means of web tools with value
co-producers during the service process. The pattern of collaborative
co-production concerning several categories of services including
knowledge based services is then discussed.
Abstract: MC (Management Control)& IC (Internal Control) – what is the relationship? (an empirical study into the definitions between MC and IC) based on the wider considerations of Internal Control and Management Control terms, attention is focused not only on the financial aspects but also more on the soft aspects of the business, such as culture, behaviour, standards and values. The limited considerations of Management Control are focused mainly in the hard, financial aspects of business operation. The definitions of Management Control and Internal Control are often used interchangeably and the results of this empirical study reveal that Management Control is part of Internal Control, there is no causal link between the two concepts. Based on the interpretation of the respondents, the term Management Control has moved from a broad term to a more limited term with the soft aspects of the influencing of behaviour, performance measurements, incentives and culture. This paper is an exploratory study based on qualitative research and on a qualitative matrix method analysis of the thematic definition of the terms Management Control and Internal Control.
Abstract: In this study, the numerical solution of unsteady flow
between two concentric rotating spheres with suction and blowing at
their boundaries is presented. The spheres are rotating about a
common axis of rotation while their angular velocities are constant.
The Navier-Stokes equations are solved by employing the finite
difference method and implicit scheme. The resulting flow patterns
are presented for various values of the flow parameters including
rotational Reynolds number Re , and a blowing/suction Reynolds
number Rew . Viscous torques at the inner and the outer spheres are
calculated, too. It is seen that increasing the amount of suction and
blowing decrease the size of eddies generated in the annulus.
Abstract: Rural tourism has many economical, environmental, and socio-cultural benefits. However, the development of rural tourism compared to urban tourism is also faced with several challenges added to the disadvantages of rural tourism. The aim of this study is to design a model of the factors affecting the motivations of rural tourists, in an attempt to improve the understanding of rural tourism motivation for the development of that form of tourism. The proposed model is based on a sound theoretical framework. It was designed following a literature review of tourism motivation theoretical frameworks and of rural tourism motivation factors. The tourism motivation theoretical framework that fitted to the best all rural tourism motivation factors was then chosen as the basis for the proposed model. This study hence found that the push and pull tourism motivation framework and the inner and outer directed values theory are the most adequate theoretical frameworks for the modeling of rural tourism motivation.
Abstract: In this research, we propose to use the discrete cosine
transform to approximate the cumulative distributions of data cube
cells- values. The cosine transform is known to have a good energy
compaction property and thus can approximate data distribution
functions easily with small number of coefficients. The derived
estimator is accurate and easy to update. We perform experiments to
compare its performance with a well-known technique - the (Haar)
wavelet. The experimental results show that the cosine transform
performs much better than the wavelet in estimation accuracy, speed,
space efficiency, and update easiness.
Abstract: Web usage mining algorithms have been widely
utilized for modeling user web navigation behavior. In this study we
advance a model for mining of user-s navigation pattern. The model
makes user model based on expectation-maximization (EM)
algorithm.An EM algorithm is used in statistics for finding maximum
likelihood estimates of parameters in probabilistic models, where the
model depends on unobserved latent variables. The experimental
results represent that by decreasing the number of clusters, the log
likelihood converges toward lower values and probability of the
largest cluster will be decreased while the number of the clusters
increases in each treatment.
Abstract: This paper proposes a new decision making approch
based on quantitative possibilistic influence diagrams which are
extension of standard influence diagrams in the possibilistic framework.
We will in particular treat the case where several expert
opinions relative to value nodes are available. An initial expert assigns
confidence degrees to other experts and fixes a similarity threshold
that provided possibility distributions should respect. To illustrate our
approach an evaluation algorithm for these multi-source possibilistic
influence diagrams will also be proposed.
Abstract: Water has always been a very precious resource.
However, many of us do not fully understand or appreciate water-s
value until there will be a shortage. We intended to analyze the water
consumption into the Spanish households to understand their
behavior according to the habitants of the house. In this research was
carried out a survey of users, asking for water consumption of their
households. The aim of this paper is get a reference value of
consumers in Spanish households to help to check their bill and
realize if their consumption is excessive, including some tips to
decrease it.
Abstract: This research’s objective is to select the model with
most accurate value by using Neural Network Technique as a way to
filter potential students who enroll in IT course by Electronic learning
at Suan Suanadha Rajabhat University. It is designed to help students
selecting the appropriate courses by themselves. The result showed
that the most accurate model was 100 Folds Cross-validation which
had 73.58% points of accuracy.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.