Abstract: Recently, an increasing number of researchers have
been focusing on working out realistic solutions to sustainability
problems. As sustainability issues gain higher importance for
organisations, the management of such decisions becomes critical.
Knowledge representation is a fundamental issue of complex
knowledge based systems. Many types of sustainability problems
would benefit from models based on experts’ knowledge. Cognitive
maps have been used for analyzing and aiding decision making. A
cognitive map can be made of almost any system or problem. A
fuzzy cognitive map (FCM) can successfully represent knowledge
and human experience, introducing concepts to represent the essential
elements and the cause and effect relationships among the concepts to
model the behaviour of any system. Integrated waste management
systems (IWMS) are complex systems that can be decomposed to
non-related and related subsystems and elements, where many factors
have to be taken into consideration that may be complementary,
contradictory, and competitive; these factors influence each other and
determine the overall decision process of the system. The goal of the
present paper is to construct an efficient IWMS which considers
various factors. The authors’ intention is to propose an expert based
system design approach for implementing expert decision support in
the area of IWMSs and introduces an appropriate methodology for
the development and analysis of group FCM. A framework for such a
methodology consisting of the development and application phases is
presented.
Abstract: The study was conducted to produce case studies from
the Malaysian public universities stands point East Coast of
Malaysia. The aim of this study is to analyze the effects of
knowledge management on human capital toward organizational
innovation. The focus point of this study is on the management
member in the faculties of these three Malaysian Public Universities
in the East Coast state of Peninsular Malaysia. In this case,
respondents who agreed to further participate in the research will be
invited to a one-hour face-to-face semi-structured, in-depth interview.
As a result, the sample size for this study was 3 deans of Faculty of
Management. Lastly, this study tries to recommend the framework of
organizational innovation in Malaysian Public Universities.
Abstract: In MANET, mobile nodes communicate with each
other using the wireless channel where transmission takes place with
significant interference. The wireless medium used in MANET is a
shared resource used by all the nodes available in MANET. Packet
reserving is one important resource management scheme which
controls the allocation of bandwidth among multiple flows through
node cooperation in MANET. This paper proposes packet reserving
and clogging control via Routing Aware Packet Reserving (RAPR)
framework in MANET. It mainly focuses the end-to-end routing
condition with maximal throughput. RAPR is complimentary system
where the packet reserving utilizes local routing information
available in each node. Path setup in RAPR estimates the security
level of the system, and symbolizes the end-to-end routing by
controlling the clogging. RAPR reaches the packet to the destination
with high probability ratio and minimal delay count. The standard
performance measures such as network security level,
communication overhead, end-to-end throughput, resource utilization
efficiency and delay measure are considered in this work. The results
reveals that the proposed packet reservation and clogging control via
Routing Aware Packet Reserving (RAPR) framework performs well
for the above said performance measures compare to the existing
methods.
Abstract: Web-based Cognitive Writing Instruction (WeCWI) is
a hybrid e-framework for the development of a web-based instruction
(WBI), which contributes towards instructional design and language
development. WeCWI divides its contribution in instructional design
into macro and micro perspectives. In macro perspective, being a 21st
century educator by disseminating knowledge and sharing ideas with
the in-class and global learners is initiated. By leveraging the virtue
of technology, WeCWI aims to transform an educator into an
aggregator, curator, publisher, social networker and ultimately, a
web-based instructor. Since the most notable contribution of
integrating technology is being a tool of teaching as well as a
stimulus for learning, WeCWI focuses on the use of contemporary
web tools based on the multiple roles played by the 21st century
educator. The micro perspective in instructional design draws
attention to the pedagogical approaches focusing on three main
aspects: reading, discussion, and writing. With the effective use of
pedagogical approaches through free reading and enterprises,
technology adds new dimensions and expands the boundaries of
learning capacity. Lastly, WeCWI also imparts the fundamental
theories and models for web-based instructors’ awareness such as
interactionist theory, cognitive information processing (CIP) theory,
computer-mediated communication (CMC), e-learning interactionalbased
model, inquiry models, sensory mind model, and leaning styles
model.
Abstract: This paper presents a model predictive control (MPC)
of a utility interactive (UI) single phase inverter (SPI) for a
photovoltaic (PV) system at residential/distribution level. The
proposed model uses single-phase phase locked loop (PLL) to
synchronize SPI with the grid and performs MPC control in a dq
reference frame. SPI model consists of boost converter (BC),
maximum power point tracking (MPPT) control, and a full bridge
(FB) voltage source inverter (VSI). No PI regulators to tune and
carrier and modulating waves are required to produce switching
sequence. Instead, the operational model of VSI is used to synthesize
sinusoidal current and track the reference. Model is validated using a
three kW PV system at the input of UI-SPI in Matlab/Simulink.
Implementation and results demonstrate simplicity and accuracy, as
well as reliability of the model.
Abstract: The fundamental issues in ICT Governance (ICTG)
implementation for Malaysian Public Sector (MPS) is how ICT be
applied to support improvements in productivity, management
effectiveness and the quality of services offered to its citizens. Our
main concern is to develop and adopt a common definition and
framework to illustrate how ICTG can be used to better align ICT
with government’s operations and strategic focus. In particular, we
want to identify and categorize factors that drive a successful ICTG
process. This paper presents the results of an exploratory study to
identify, validate and refine such Critical Success Factors (CSFs) and
confirmed seven CSFs and nineteen sub-factors as influential factors
that fit MPS after further validated and refined. The Delphi method
applied in validation and refining process before being endorsed as
appropriate for MPS. The identified CSFs reflect the focus areas that
need to be considered strategically to strengthen ICT Governance
implementation and ensure business success.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: The emergence of the Semantic Web technology
increases day by day due to the rapid growth of multiple web pages.
Many standard formats are available to store the semantic web data.
The most popular format is the Resource Description Framework
(RDF). Querying large RDF graphs becomes a tedious procedure
with a vast increase in the amount of data. The problem of query
optimization becomes an issue in querying large RDF graphs.
Choosing the best query plan reduces the amount of query execution
time. To address this problem, nature inspired algorithms can be used
as an alternative to the traditional query optimization techniques. In
this research, the optimal query plan is generated by the proposed
SAPSO algorithm which is a hybrid of Simulated Annealing (SA)
and Particle Swarm Optimization (PSO) algorithms. The proposed
SAPSO algorithm has the ability to find the local optimistic result
and it avoids the problem of local minimum. Experiments were
performed on different datasets by changing the number of predicates
and the amount of data. The proposed algorithm gives improved
results compared to existing algorithms in terms of query execution
time.
Abstract: There is an essential need for obtaining the mathematical representation of fish body undulations, which can be used for designing and building new innovative types of marine propulsion systems with less environmental impact. This research work presents a case study to derive the mathematical model for fish body movement. Observation and capturing image methods were used in this study in order to obtain a mathematical representation of Clariasbatrachus fish (catfish). An experiment was conducted by using an aquarium with dimension 0.609 m x 0.304 m x 0.304 m, and a 0.5 m ruler was attached at the base of the aquarium. Progressive Scan Monochrome Camera was positioned at 1.8 m above the base of the aquarium to provide swimming sequences. Seven points were marked on the fish body using white marker to indicate the fish movement and measuring the amplitude of undulation. Images from video recordings (20 frames/s) were analyzed frame by frame using local coordinate system, with time interval 0.05 s. The amplitudes of undulations were obtained for image analysis from each point that has been marked on fish body. A graph of amplitude of undulations versus time was plotted by using computer to derive a mathematical fit. The function for the graph is polynomial with nine orders.
Abstract: In this paper, we present a new maintenance model
for a partially observable system subject to two failure modes,
namely a catastrophic failure and a failure due to the system
degradation. The system is subject to condition monitoring and the
degradation process is described by a hidden Markov model. A
cost-optimal Bayesian control policy is developed for maintaining
the system. The control problem is formulated in the semi-Markov
decision process framework. An effective computational algorithm is
developed, illustrated by a numerical example.
Abstract: Mostly of public financing programs at national and regional level are funded from European Union sources. EU can participate directly to a national and regional program (example LEADER initiative, URBAN…) or indirectly by funding regional or national funds.Funds from European Union are provided from EU multiannual financial framework form which the annual budget is programmed. The adjusted program 2007-2013 of the EU considered commitments of almost 1 trillion Euros for the EU-28 countries. Provisions of the new program 2014-2020 consider commitments of more than 1 trillion Euros. Sustainable growth, divided to Cohesion and Competitiveness for Growth an Employment, is one of the two principal categories; the other is the preservation and management of natural resources.Through this financing process SMEs benefited of EU and public sources by receiving grants for their investments. Most of the financial instruments are available indirectly through the national financial intermediaries. Part of them is managed by the European Investment Fund.The paper focuses on the public financing to SMEs by examining case studies on divers forms of public help. It tries to distinguish the efficiency of the examined good practices and therefore try to have some conclusions on the possibility of application to other regions.
Abstract: Recently there has been a dramatic proliferation in
the number of social networking sites (SNSs) users; however, little
is published about what motivates college students to use SNSs in
education. The main goal of this research is to explore the college
students’ motives for using SNSs in education. A conceptual
framework has therefore been developed to identify the main
factors that influence/motivate students to use social networking
sites for learning purposes. To achieve the research objectives a
quantitative method was used to collect data. A questionnaire has
been distributed amongst college students. The results reveal that
social influence, perceived enjoyment, institute regulation,
perceived usefulness, ranking up-lift, attractiveness,
communication tools, free of charge, sharing material and course
nature all play an important role in the motivation of college
students to use SNSs for learning purposes.
Abstract: Virtual reality (VR) is a rapidly emerging computer
interface that attempts to immerse the user completely within an
experimental recreation; thereby, greatly enhancing the overall
impact and providing a much more intuitive link between the
computer and the human participants. The main objective of this
study is to design tractor trailer capable of meeting the customers’
requirements and suitable for rough conditions to be used in
combination with a farm tractor in India. The final concept is capable
of providing arrangements for attaching the trailer to the tractor easily
by pickup hitch, stronger and lighter supporting frame, option of
spare tyre etc. Furthermore, the resulting product design can be sent
via the Internet to customers for comments or marketing purposes.
The virtual prototyping (VP) system therefore facilitates advanced
product design and helps reduce product development time and cost
significantly.
Abstract: As a developing country, The Kingdom of Saudi Arabia (KSA) needs to make the best possible use of its workforce for social and economic reasons. The workforce is diverse, calling for appropriate diversity management (DM). The thesis focuses on the banking sector in KSA. To date, there have been no studies on DM in the banking sector in this country. Many organizations have introduced specific policies and programmes to improve the recruitment, inclusion, promotion, and retention of diverse employees, in addition to the legal requirements existing in many countries. However, Western-centric models of DM may not be applicable, at least not in their entirety, in other regions.
The aim of the study is to devise a framework for understanding gender, age and disability DM in the banking sector in KSA in order to enhance DM in this sector. A sample of 24 managers, 2 from each of the 12 banks, was interviewed to obtain their views on DM in the banking sector in KSA. Thematic analysis was used to analyze the data. These themes were used to develop the questionnaire, which was administered to 10 managers in each of the 12 banks. After analysis of these data, and completion of the study, the research will make a theoretical contribution to the knowledge on DM and a practical contribution to the management of diversity in Saudi banks. This paper concerns a work in progress.
Abstract: Both knowledge economy and sustainable development are considered key dimensions in the policy action lines of many developed and developing countries. In this context, universities and other higher education institutes have a vital role in developing and sustaining wellbeing communities.
In this paper, the authors’ aim is to address the links between the concepts of innovation and entrepreneurial capacity and knowledge economy, and to utilize the approach of intellectual capital development in building a sustainable knowledge economy.
The paper will contribute to two discourses:
Developing a common understanding of the intersection aspects between the three concepts: Knowledge economy, Innovation and entrepreneurial system, and sustainable development.
Paving the road towards developing an integrated multidimensional framework for sustainable knowledge economy.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.
Abstract: The process in which the complementary information from multiple images is integrated to provide composite image that contains more information than the original input images is called image fusion. Medical image fusion provides useful information from multimodality medical images that provides additional information to the doctor for diagnosis of diseases in a better way. This paper represents the wavelet based medical image fusion algorithm on different multimodality medical images. In order to fuse the medical images, images are decomposed using Redundant Wavelet Transform (RWT). The high frequency coefficients are convolved with morphological operator followed by the maximum-selection (MS) rule. The low frequency coefficients are processed by MS rule. The reconstructed image is obtained by inverse RWT. The quantitative measures which includes Mean, Standard Deviation, Average Gradient, Spatial frequency, Edge based Similarity Measures are considered for evaluating the fused images. The performance of this proposed method is compared with Pixel averaging, PCA, and DWT fusion methods. When compared with conventional methods, the proposed framework provides better performance for analysis of multimodality medical images.