Abstract: Risk analysis is considered as a fundamental aspect
relevant for ensuring the level of critical infrastructure protection,
where the critical infrastructure is seen as system, asset or its part
which is important for maintaining the vital societal functions. Article
actually discusses and analyzes the potential application of selected
tools of information support for the implementation and within the
framework of risk analysis and critical infrastructure protection. Use
of the information in relation to their risk analysis can be viewed as a
form of simplifying the analytical process. It is clear that these
instruments (information support) for these purposes are countless, so
they were selected representatives who have already been applied in
the selected area of critical infrastructure, or they can be used. All
presented fact were the basis for critical infrastructure resilience
evaluation methodology development.
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.
Abstract: Constructing a portfolio of investments is one of the
most significant financial decisions facing individuals and
institutions. In accordance with the modern portfolio theory
maximization of return at minimal risk should be the investment goal
of any successful investor. In addition, the costs incurred when
setting up a new portfolio or rebalancing an existing portfolio must
be included in any realistic analysis.
In this paper rebalancing an investment portfolio in the presence of
transaction costs on the Croatian capital market is analyzed. The
model applied in the paper is an extension of the standard portfolio
mean-variance optimization model in which transaction costs are
incurred to rebalance an investment portfolio. This model allows
different costs for different securities, and different costs for buying
and selling. In order to find efficient portfolio, using this model, first,
the solution of quadratic programming problem of similar size to the
Markowitz model, and then the solution of a linear programming
problem have to be found. Furthermore, in the paper the impact of
transaction costs on the efficient frontier is investigated. Moreover, it
is shown that global minimum variance portfolio on the efficient
frontier always has the same level of the risk regardless of the amount
of transaction costs. Although efficient frontier position depends of
both transaction costs amount and initial portfolio it can be concluded
that extreme right portfolio on the efficient frontier always contains
only one stock with the highest expected return and the highest risk.
Abstract: It has become an increasing evident that large
development influences the climate. There are concerns that rising
temperature over developed areas could have negative impact and
increase living discomfort within city boundaries. Temperature trends
in Ibadan city have received little attention, yet the area has
experienced heavy urban expansion between 1972 and 2014. This
research aims at examining the impact of landuse change on surface
temperature knowing that the built-up environment absorb and store
solar energy, resulting into the Urban Heat Island (UHI) effect. The
Landsat imagery was used to examine the landuse change for a
period of 42 years (1972-2014). Land Surface Temperature (LST)
was obtained by converting the thermal band to a surface temperature
map and zonal statistic analyses was used to examine the relationship
between landuse and temperature emission. The results showed that
the settlement area increased to a large extent while the area covered
by vegetation reduced during the study period. The spatial and
temporal trends of surface temperature are related to the gradual
change in urban landuse/landcover and the settlement area has the
highest emission. This research provides useful insight into the
temporal behavior of the Ibadan city.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.
Abstract: The performance and analysis of speech recognition
system is illustrated in this paper. An approach to recognize the
English word corresponding to digit (0-9) spoken by 2 different
speakers is captured in noise free environment. For feature extraction,
speech Mel frequency cepstral coefficients (MFCC) has been used
which gives a set of feature vectors from recorded speech samples.
Neural network model is used to enhance the recognition
performance. Feed forward neural network with back propagation
algorithm model is used. However other speech recognition
techniques such as HMM, DTW exist. All experiments are carried
out on Matlab.
Abstract: Experimental & numeral study of temperature
distribution during milling process, is important in milling quality
and tools life aspects. In the present study the milling cross-section
temperature is determined by using Artificial Neural Networks
(ANN) according to the temperature of certain points of the work
piece and the point specifications and the milling rotational speed of
the blade. In the present work, at first three-dimensional model of the
work piece is provided and then by using the Computational Heat
Transfer (CHT) simulations, temperature in different nods of the
work piece are specified in steady-state conditions. Results obtained
from CHT are used for training and testing the ANN approach. Using
reverse engineering and setting the desired x, y, z and the milling
rotational speed of the blade as input data to the network, the milling
surface temperature determined by neural network is presented as
output data. The desired points temperature for different milling
blade rotational speed are obtained experimentally and by
extrapolation method for the milling surface temperature is obtained
and a comparison is performed among the soft programming ANN,
CHT results and experimental data and it is observed that ANN soft
programming code can be used more efficiently to determine the
temperature in a milling process.
Abstract: Although there had been a many studies that shows
the impact of air pollution on physical health, comparatively less was
known of human behavioral responses and annoyance impacts.
Annoyance caused by air pollution is a public health problem because
it can be an ambient stressor causing stress and disease and can affect
quality of life. The objective of this work is to evaluate the
annoyance caused by air pollution in two different industrialized
urban areas, Dunkirk (France) and Vitoria (Brazil). The populations
of these cities often report feeling annoyed by dust. Surveys were
conducted, and the collected data were analyzed using statistical
analyses. The results show that sociodemographic variables,
importance of air quality, perceived industrial risk, perceived air
pollution and occurrence of health problems play important roles in
the perceived annoyance. These results show the existence of a
common problem in geographically distant areas and allow
stakeholders to develop prevention strategies.
Abstract: The purpose of this presentation is to describe an interdisciplinary teaching program that integrates physical education concepts using a philosophical approach. The presentation includes a review of: a) the philosophy of American education, b) the philosophy of sports and physical education, c) the interdisciplinary physical education program, d) professional development programs, (e) the Success of this physical education program, f) future of physical education. This unique interdisciplinary program has been implemented in an urban school physical education discipline in East Orange, New Jersey for over 10 years.
During the program the students realize that the bodies go through different experiences. The body becomes a place where a child can recognize in an enjoyable way to express and perceive particular feelings or mental states. Children may distinguish themselves to have high abilities in the social or other domains but low abilities in the field of athletics.
The goal of this program for the individuals is to discover new skills, develop and demonstrate age appropriate mastery level at different tasks, therefore the program consists of 9 to 12 sports, including many game. Each successful experience increases the awareness ability. Engaging in sports and physical activities are social movements involving groups of children in situations such as teams, friends, and recreational settings, which serve as a primary socializing agent for teaching interpersonal skills. As a result of this presentation the audience will reflect and explore how to structure a physical education program to integrate interdisciplinary subjects with philosophical concepts.
Abstract: The decision-making process is theoretically clearly
defined. Generally, it includes the problem identification and
analysis, data gathering, goals and criteria setting, alternatives
development and optimal alternative choice and its implementation.
In practice however, various modifications of the theoretical
decision-making process can occur. The managers can consider some
of the phases to be too complicated or unfeasible and thus they do not
carry them out and conversely some of the steps can be
overestimated.
The aim of the paper is to reveal and characterize the perception of
the individual phases of decision-making process by the managers.
The research is concerned with managers in the military environment
– commanders. Quantitative survey is focused cross-sectionally in the
individual levels of management of the Ministry of Defence of the
Czech Republic. On the total number of 135 respondents the analysis
focuses on which of the decision-making process phases are
problematic or not carried out in practice and which are again
perceived to be the easiest. Then it is examined the reasons of the
findings.
Abstract: In reference to the legal state in the Thai legal system,
most people understand the minor principles of the legal state form,
which are the principles that can be explained and understood easily
and the results can be seen clearly, especially in the legitimacy of
administrative acts. Therefore, there is no awareness of justice, which
is the fundamental value of Thai law. The legitimacy of administrative
acts requires the administration to adhere to the constitution and
legislative laws in enforcement of the laws. If it appears that the
administrative acts are illegitimate, the administrative court, as the
court of justice, will revoke those acts as if they had never been set in
the legal system, this will affect people’s trust as they are unaware as
to whether the administrative acts that appoint their lives are
legitimate or not. Regarding the revocation of administrative orders
by the administrative court as if those orders had never existed, the
common individual surely cannot be expected to comprehend the
security of their juristic position. Therefore, the legal state does not
require a revocation of the government’s acts to terminate its legal
results merely because those acts are illegitimate, but there should be
considerations and realizations regarding the “The Principle of the
Protection of Legitimate Expectation,” which is a minor principle in
the legal state’s content that focuses on supporting and protecting
legitimate expectations of the juristic position of an individual and
maintaining justice, which is the fundamental value of Thai law.
Abstract: In this paper, we consider the vehicle routing problem
with mixed fleet of conventional and heterogenous electric vehicles
and time dependent charging costs, denoted VRP-HFCC, in which
a set of geographically scattered customers have to be served by a
mixed fleet of vehicles composed of a heterogenous fleet of Electric
Vehicles (EVs), having different battery capacities and operating
costs, and Conventional Vehicles (CVs). We include the possibility
of charging EVs in the available charging stations during the routes
in order to serve all customers. Each charging station offers charging
service with a known technology of chargers and time dependent
charging costs. Charging stations are also subject to operating time
windows constraints. EVs are not necessarily compatible with all
available charging technologies and a partial charging is allowed.
Intermittent charging at the depot is also allowed provided that
constraints related to the electricity grid are satisfied.
The objective is to minimize the number of employed vehicles and
then minimize the total travel and charging costs.
In this study, we present a Mixed Integer Programming Model and
develop a Charging Routing Heuristic and a Local Search Heuristic
based on the Inject-Eject routine with different insertion methods. All
heuristics are tested on real data instances.
Abstract: It is likely that robots will cross the boundaries of
industry into households over the next decades. With demographic
challenges worldwide, the future ageing populations will require the
introduction of assistive technologies capable of providing, care,
human dignity and quality of life through the aging process. Robotics
technology has a high potential for being used in the areas of social
and healthcare by promoting a wide range of activities such as
entertainment, companionship, supervision or cognitive and physical
assistance. However such close Human Robotics Interaction (HRI)
encompass a rich set of ethical scenarios that need to be addressed
before Socially Assistive Robots (SARs) reach the global markets.
Such interactions with robots may seem a worthy goal for many
technical/financial reasons but inevitably require close attention to
the ethical dimensions of such interactions. This article investigates
the current HRI benchmark of social success. It revises it according
to the ethical principles of beneficence, non-maleficence and justice
aligned with social care ethos. An extension of such benchmark is
proposed based on an empirical study of HRIs conducted with elderly
groups.
Abstract: Governments collect and produce large amounts of
data. Increasingly, governments worldwide have started to implement
open data initiatives and also launch open data portals to enable the
release of these data in open and reusable formats. Therefore, a large
number of open data repositories, catalogues and portals have been
emerging in the world. The greater availability of interoperable and
linkable open government data catalyzes secondary use of such data,
so they can be used for building useful applications which leverage
their value, allow insight, provide access to government services, and
support transparency. The efficient development of successful open
data portals makes it necessary to evaluate them systematic, in order
to understand them better and assess the various types of value they
generate, and identify the required improvements for increasing this
value. Thus, the attention of this paper is directed particularly to the
field of open data portals. The main aim of this paper is to compare
the selected open data portals on the national level using content
analysis and propose a new evaluation framework, which further
improves the quality of these portals. It also establishes a set of
considerations for involving businesses and citizens to create eservices
and applications that leverage on the datasets available from
these portals.
Abstract: Our goal is development of an algorithm capable of
predicting the directional trend of the Standard and Poor’s 500 index
(S&P 500). Extensive research has been published attempting to
predict different financial markets using historical data testing on an
in-sample and trend basis, with many authors employing excessively
complex mathematical techniques. In reviewing and evaluating these
in-sample methodologies, it became evident that this approach was
unable to achieve sufficiently reliable prediction performance for
commercial exploitation. For these reasons, we moved to an out-ofsample
strategy based on linear regression analysis of an extensive
set of financial data correlated with historical closing prices of the
S&P 500. We are pleased to report a directional trend accuracy of
greater than 55% for tomorrow (t+1) in predicting the S&P 500.
Abstract: In recent years, the hair building fiber has become
popular, in other words, it is an effective method which helps people
who suffer hair loss or sparse hair since the hair building fiber is
capable to create a natural look of simulated hair rapidly. In the
markets, there are a lot of hair fiber brands that have been designed to
formulate an intense bond with hair strands and make the hair appear
more voluminous instantly. However, those products have their own
set of properties. Thus, in this report, some measurement techniques
are proposed to identify those products. Up to five different brands of
hair fiber are tested. The electrostatic and dielectric properties of the
hair fibers are macroscopically tested using design DC and high
frequency microwave techniques. Besides, the hair fibers are
microscopically analysis by magnifying the structures of the fiber
using scanning electron microscope (SEM). From the SEM photos,
the comparison of the uniformly shaped and broken rate of the hair
fibers in the different bulk samples can be observed respectively.
Abstract: Many aluminum motorcycle parts produced by a high
pressure die casting. Some parts such as fuel caps were a thin and
complex shape. This part risked for porosities and blisters on surface
if it only depended on an experience of mold makers for mold design.
This research attempted to use CAST-DESIGNER software
simulated the high pressure die casting process with the same process
parameters of a motorcycle fuel cap production. The simulated results
were compared with fuel cap products and expressed the same
porosity and blister locations on cap surface. An average of absolute
difference of simulated results was obtained 0.094 mm when
compared the simulated porosity and blister defect sizes on the fuel
cap surfaces with the experimental micro photography. This
comparison confirmed an accuracy of software and will use the
setting parameters to improve fuel cap molds in the further work.
Abstract: The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
Abstract: The organizations in the knowledge economy era have
recognized the importance of building knowledge assets for
sustainable growth and development. In comparison to other
industries, Information Technology (IT) enterprises, holds an edge in
developing an effective Knowledge Management (KM) programmethanks
to their in-house technological abilities. This paper tries to
study the various knowledge based incentive programmes and its
effect on Knowledge Sharing and Learning in the context of the
Indian IT sector. A conceptual model is developed linking KM
Incentives, Knowledge Sharing and Learning. A questionnaire study
is conducted to collect primary data from the knowledge workers of
the IT organizations located in India. The data was analysed using
Structural Equation Modeling using Partial Least Square method. The
results show a strong influence of knowledge management incentives
on knowledge sharing and an indirect influence on learning.
Abstract: In this paper, an analysis of some model order
reduction techniques is presented. A new hybrid algorithm for model
order reduction of linear time invariant systems is compared with the
conventional techniques namely Balanced Truncation, Hankel Norm
reduction and Dominant Pole Algorithm (DPA). The proposed hybrid
algorithm is known as Clustering Dominant Pole Algorithm (CDPA),
is able to compute the full set of dominant poles and its cluster center
efficiently. The dominant poles of a transfer function are specific
eigenvalues of the state space matrix of the corresponding dynamical
system. The effectiveness of this novel technique is shown through
the simulation results.