Abstract: Possible advantages of technology in educational
context required the defining boundaries of formal and informal
learning. Increasing opportunity to ubiquitous learning by
technological support has revealed a question of how to discover
the potential of individuals in the spontaneous environments such as
social networks. This seems to be related with the question of what
purposes in social networks have been being used? Social networks
provide various advantages in educational context as collaboration,
knowledge sharing, common interests, active participation and
reflective thinking. As a consequence of these, the purpose of this
study is composed of proposing a new model that could determine
factors which effect adoption of social network applications for usage
in educational context. While developing a model proposal, the
existing adoption and diffusion models have been reviewed and they
are thought to be suitable on handling an original perspective instead
of using completely other diffusion or acceptance models because of
different natures of education from other organizations. In the
proposed model; social factors, perceived ease of use, perceived
usefulness and innovativeness are determined four direct constructs
that effect adoption process. Facilitating conditions, image,
subjective norms and community identity are incorporated to model
as antecedents of these direct four constructs.
Abstract: Software Reliability is one of the key factors in the software development process. Software Reliability is estimated using reliability models based on Non Homogenous Poisson Process. In most of the literature the Software Reliability is predicted only in testing phase. So it leads to wrong decision-making concept. In this paper, two Software Reliability concepts, testing and operational phase are studied in detail. Using S-Shaped Software Reliability Growth Model (SRGM) and Exponential SRGM, the testing and operational reliability values are obtained. Finally two reliability values are compared and optimal release time is investigated.
Abstract: There are various approaches to implement quality
improvements. Organizations aim for a management standard which
is capable of providing customers with quality assurance on their
product/service via continuous process improvement. Carefully
planned steps are necessary to ensure the right quality improvement
methodology (QIM) and business operations are consistent, reliable
and truly meet the customers' needs. This paper traces the evolution
of QIM in Malaysia-s Information Technology (IT) industry in the
past, current and future; and highlights some of the thought of
researchers who contributed to the science and practice of quality,
and identifies leading methodologies in use today. Some of the
misconceptions and mistakes leading to quality system failures will
also be examined and discussed. This paper aims to provide a general
overview of different types of QIMs available for IT businesses in
maximizing business advantages, enhancing product quality,
improving process routines and increasing performance earnings.
Abstract: Green Lean Total Quality Management (LTQM) Human Resource Management (HRM) System is a system comprises of HRM in Environmental Management System (EMS) practices which is integrated to TQM with Lean Manufacturing (LM) principles. HRM is essential especially in dealing with low motivation and less productive employees. The ultimate goal of this system is to focus on achieving total human resource development that is motivated and capable to optimize their creativity to be a part of Green and Lean TQM organization. A survey questionnaire was developed and distributed to 30 highly active automotive vendors in Malaysia and analyzed by Minitab v16 and SPSS v17. It was found out companies that are practicing Green LTQM HRM practices have generated more revenue and have RND capability. However, years of company establishment do not affect the openness of the company to adapt new initiatives that can help to improve the effectiveness of the operations. It was also found out the importance of training, communication and rewards for employees. The Green LTQM HRM practices framework model established in this study hopefully will give preliminary insight especially to companies that are still looking for system that can improve their productivity from managing human resource. This is preliminary study that combined 4 awards practices, ISO/TS16949, Toyota Production System SAEJ4000, MAJAICO Lean Production System and EMS focusing on highly active companies that have been involved in MAJAICO Program and Proton Vendor Development Program. Future study can be conducted to know the status at other industry as well as case study pertaining to this system.
Abstract: In this study, hydroxyapatite (HA) composites are
prepared on addition of 30%CaO-30%P2O5-40%Na2 O based glass to
pure HA, in proportion of 2, 5, and 10 wt %. Each composition was
sintered over a range of temperatures. The quantitative phase
analysis was carried out using XRD and the microstructures were
studied using SEM. The density, microhardness, and compressive
strength have shown increase with the increasing amount of glass
addition. The resulting composites have chemical compositions that
are similar to the inorganic constituent of the mineral part of bone,
and constitutes trace elements like Na. X-ray diffraction showed no
decomposition of HA to secondary phases, however, the glass
reinforced-HA composites contained a HA phase and variable
amounts of tricalcium phosphate phase, depending on the amount of
bioglass added. The HA-composite material exhibited higher
compressive strength compared to sintered HA. The HA composite
reinforced with 10 wt % bioglass showed highest bioactivity level.
Abstract: Cognizant of the fact that enterprise systems involve
organizational change and their implementation is over shadowed by a
high failure rate, it is argued that there is the need to focus attention on
employees- perceptions of such organizational change when
explaining adoption behavior of enterprise systems. For this purpose,
the research incorporates a conceptual constructo fattitude toward
change that captures views about the need for organizational change.
Centered on this conceptual construct, the research model includes
beliefs regarding the system and behavioral intention as its
consequences, and the personal characteristics of organizational
commitment and perceived personal competence as its antecedents.
Structural equation analysis using LISREL provides significant
support for the proposed relationships. Theoretical and practical
implications are discussed along with limitations.
Abstract: Chemical industry project management involves
complex decision making situations that require discerning abilities
and methods to make sound decisions. Project managers are faced
with decision environments and problems in projects that are
complex. In this work, case study is Research and Development
(R&D) project selection. R&D is an ongoing process for forward
thinking technology-based chemical industries. R&D project
selection is an important task for organizations with R&D project
management. It is a multi-criteria problem which includes both
tangible and intangible factors. The ability to make sound decisions
is very important to success of R&D projects. Multiple-criteria
decision making (MCDM) approaches are major parts of decision
theory and analysis. This paper presents all of MCDM approaches
for use in R&D project selection. It is hoped that this work will
provide a ready reference on MCDM and this will encourage the
application of the MCDM by chemical engineering management.
Abstract: Electronic Government is one of the special concepts
which has been performed successfully within recent decades.
Electronic government is a digital, wall-free government with a
virtual organization for presenting of online governmental services
and further cooperation in different political/social activities. In order
to have a successful implementation of electronic government
strategy and benefiting from its complete potential and benefits and
generally for establishment and applying of electronic government, it
is necessary to have different infrastructures as the basics of
electronic government with lack of which it is impossible to benefit
from mentioned services. For this purpose, in this paper we have
managed to recognize relevant obstacles for establishment of
electronic government in Iran. All required data for recognition of
obstacles were collected from statistical society of involved
specialists of Ministry of Communications & Information
Technology of Iran and Information Technology Organization of
Tehran Municipality through questionnaire. Then by considering of
five-point Likert scope and μ =3 as the index of relevant factors of
proposed model, we could specify current obstacles against
electronic government in Iran along with some guidelines and
proposal in this regard. According to the results, mentioned obstacles
for applying of electronic government in Iran are as follows:
Technical & technological problems, Legal, judicial & safety
problems, Economic problems and Humanistic Problems.
Abstract: While the form of crises may change, their essence
remains the same (such as a cycle of abundant liquidity, rapid credit
growth, and a low-inflation environment followed by an asset-price
bubble). The current market turbulence began in mid-2000s when the
US economy shifted to imbalanced both internal and external
macroeconomic positions. We see two key causes of these problems
– loose US monetary policy in early 2000s and US government
guarantees issued on the securities by government-sponsored
enterprises what was further fueled by financial innovations such as
structured credit products. We have discovered both negative and
positive lessons deriving from this crisis and divided the negative
lessons into three groups: financial products and valuation, processes
and business models, and strategic issues. Moreover, we address key
risk management lessons and exit strategies derived from the current
crisis and recommend policies that should help diminish the negative
impact of future potential crises.
Abstract: MATCH project [1] entitle the development of an
automatic diagnosis system that aims to support treatment of colon
cancer diseases by discovering mutations that occurs to tumour
suppressor genes (TSGs) and contributes to the development of
cancerous tumours. The constitution of the system is based on a)
colon cancer clinical data and b) biological information that will be
derived by data mining techniques from genomic and proteomic
sources The core mining module will consist of the popular, well
tested hybrid feature extraction methods, and new combined
algorithms, designed especially for the project. Elements of rough
sets, evolutionary computing, cluster analysis, self-organization maps
and association rules will be used to discover the annotations
between genes, and their influence on tumours [2]-[11].
The methods used to process the data have to address their high
complexity, potential inconsistency and problems of dealing with the
missing values. They must integrate all the useful information
necessary to solve the expert's question. For this purpose, the system
has to learn from data, or be able to interactively specify by a domain
specialist, the part of the knowledge structure it needs to answer a
given query. The program should also take into account the
importance/rank of the particular parts of data it analyses, and adjusts
the used algorithms accordingly.
Abstract: In this work, the physical based device model of
AlGaN/GaN high electron mobility transistors (HEMTs) has been
established and the corresponding device operation behavior has
been investigated also by using Sentaurus TCAD from Synopsys.
Advanced AlGaN/GaN hetero-structures with GaN cap layer and AlN
spacer have been considered and the GaN cap layer and AlN spacer
are found taking important roles on the gate leakage blocking and
off-state breakdown voltage enhancement.
Abstract: Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optmize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is also able to automatically suggest a strategy for number of classes optimization.The tool is used to classify macroeconomic data that report the most developed countries? import and export. It is possible to classify the countries based on their economic behaviour and use an ad hoc tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation.
Abstract: Despite of many scholars and practitioners recognize
the knowledge management implementation in an organizations but
insufficient attention has been paid by researchers to select suitable
knowledge portal system (KPS) selection. This study develops a
Multi Criteria Decision making model based on the fuzzy VIKOR
approach to help organizations in selecting KPS. The suitable portal
is the critical influential factors on the success of knowledge
management (KM) implementation in an organization.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: 17α-ethynylestradiol (EE2) is a synthetic estrogen
used as a key ingredient in an oral contraceptives pill. EE2 is an
endocrine disrupting compound, high in estrogenic potency.
Although EE2 exhibits low degree of biodegradability with common
microorganisms in wastewater treatment plants (WWTPs), this
compound can be biotransformed by ammonia-oxidizing bacteria
(AOB) via a co-metabolism mechanism in WWTPs. This study
aimed to investigate the effect of real wastewater on
biotransformation of EE2 by AOB. A preliminary experiment on the
effect of nitrite and pH levels on abiotic transformation of EE2
suggested that the abiotic transformation occurred at only pH
Abstract: Data objects are usually organized hierarchically, and
the relations between them are analyzed based on a corresponding
concept hierarchy. The relation between data objects, for example how
similar they are, are usually analyzed based on the conceptual distance
in the hierarchy. If a node is an ancestor of another node, it is enough
to analyze how close they are by calculating the distance vertically.
However, if there is not such relation between two nodes, the vertical
distance cannot express their relation explicitly. This paper tries to fill
this gap by improving the analysis method for data objects based on
hierarchy. The contributions of this paper include: (1) proposing an
improved method to evaluate the vertical distance between concepts;
(2) defining the concept horizontal distance and a method to calculate
the horizontal distance; and (3) discussing the methods to confine a
range by the horizontal distance and the vertical distance, and
evaluating the relation between concepts.
Abstract: Dredged sediment (DS) was utilized as source of
silt-clay and organic matter in artificially prepared eelgrass substrates with mountain sand (MS) as the sand media. Addition of DS showed
improved growth of eelgrass in the mixed substrates. Increase in added
DS up to 15% silt-clay showed increased shoot growth but additional
DS in 20% silt-clay mixture didn-t result to further increase in eelgrass
growth. Improved root establishment were also found for plants in pots
with added DS as shown by the increased resistance to uprooting, increased number of rhizome nodes and longer roots. Results demonstrated that addition of DS may be beneficial to eelgrass up to a
certain extent only and too much of it might be harmful to eelgrass plants.
Abstract: Experimental investigations were carried out in the
Manchester Tidal flow Facility (MTF) to study the flow patterns in
the region around and adjacent to a hypothetical headland in tidal
(oscillatory) ambient flow. The Planar laser-induced fluorescence
(PLIF) technique was used for visualization, with fluorescent dye
released at specific points around the headland perimeter and in its
adjacent recirculation zone. The flow patterns can be generalized into
the acceleration, stable flow and deceleration stages for each halfcycle,
with small variations according to location, which are more
distinct for low Keulegan-Carpenter number (KC) cases. Flow
patterns in the mixing region are unstable and complex, especially in
the recirculation zone. The flow patterns are in agreement with
previous visualizations, and support previous results in steady
ambient flow. It is suggested that the headland lee could be a viable
location for siting of pollutant outfalls.
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.
Abstract: In the past few decades, researchers have witnessed a
paradigm shift in Human Resource Management-from individual
performance to organizational outcomes with the role of Human
resource (HR) managers becoming increasingly significant to the
organization. In such a context, it is important to examine HR
practices from a strategic perspective on the sustained competitive
advantage (SCA) of the organizations. The present study explores
how Indian organisations look at their human resources strategically
when faced with competitive environment. Also, it explores strategic
initiatives being taken to manage human resources within the
organisations and how these initiatives promote SCA in terms of
enhancing the overall customer-centric delivery of goods and
services.