Abstract: The beginning of 21st century has witnessed new
advancements in the design and use of new materials for biosensing
applications, from nano to macro, protein to tissue. Traditional
analytical methods lack a complete toolset to describe the
complexities introduced by living systems, pathological relations,
discrete hierarchical materials, cross-phase interactions, and
structure-property dependencies. Materiomics – via systematic
molecular dynamics (MD) simulation – can provide structureprocess-
property relations by using a materials science approach
linking mechanisms across scales and enables oriented biosensor
design. With this approach, DNA biosensors can be utilized to detect
disease biomarkers present in individuals’ breath such as acetone for
diabetes. Our wireless sensor array based on single-stranded DNA
(ssDNA)-decorated single-walled carbon nanotubes (SWNT) has
successfully detected trace amount of various chemicals in vapor
differentiated by pattern recognition. Here, we present how MD
simulation can revolutionize the way of design and screening of DNA
aptamers for targeting biomarkers related to oral diseases and oral
health monitoring. It demonstrates great potential to be utilized to
build a library of DNDA sequences for reliable detection of several
biomarkers of one specific disease, and as well provides a new
methodology of creating, designing, and applying of biosensors.
Abstract: Visibility problems are central to many computational geometry applications. One of the typical visibility problems is computing the view from a given point. In this paper, a linear time procedure is proposed to compute the visibility subsets from a corner of a rectangular prism in an orthogonal polyhedron. The proposed algorithm could be useful to solve classic 3D problems.
Abstract: In this research, we propose to conduct diagnostic and
predictive analysis about the key factors and consequences of urban
population relocation. To achieve this goal, urban simulation models
extract the urban development trends as land use change patterns from
a variety of data sources. The results are treated as part of urban big
data with other information such as population change and economic
conditions. Multiple data mining methods are deployed on this data to
analyze nonlinear relationships between parameters. The result
determines the driving force of population relocation with respect to
urban sprawl and urban sustainability and their related parameters.
This work sets the stage for developing a comprehensive urban
simulation model for catering to specific questions by targeted users. It
contributes towards achieving sustainability as a whole.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: The number and adequacy of Performance-Indicators
(PIs) for organisational purposes are core to the success of
organisations and a major concern to the sponsor of this research.
This assignment developed a procedure to improve a firm’s
performance assessment system, by identifying two key-PIs out of 28
initial ones, and by setting criteria and their relative importance to
validate and rank the adequacy and the right number of operational
metrics. The Analytical-Hierarchy-Process was used with a synthesismethod
to treat data coming from the management inquiries.
Although organisational alignment has been achieved, business
processes should also be targeted and PIs continuously revised.
Abstract: The paper deals with possibilities of increase train
capacity by using a new type of railway wagon. In the first part is
created a mathematical model to calculate the capacity of the train.
The model is based on the main limiting parameters of the train -
maximum number of axles per train, maximum gross weight of train,
maximum length of train and number of TEUs per one wagon. In the
second part is the model applied to four different model trains with
different composition of the train set and three different average
weights of TEU and a train consisting of a new type of wagons. The
result is to identify where the carrying capacity of the original trains
is higher, respectively less than a capacity of train consisting of a new
type of wagons.
Abstract: The overall goal of this paper is to examine the
suitability and potential of the policies addressing the sustainability
and affordability of housing for returnees, and to determine the impact
of this policy on housing delivery for Afghan refugees. Housing is a
central component of the settlement experience of refugees. A positive
housing situation can facilitate many aspects of integration.
Unaffordable, and unsafe housing, however, can cause disruptions in
the entire settlement process. This paper aims to identify a suite of
built forms for housing that is both affordable and environmentally
sustainable for Afghan refugees. The result was the development of a
framework that enables the assessment of the overall performance of
various types of housing development in all zones of the country.
There is very little evidence that the present approach of housing
provision to the vagaries of market forces has provided affordable
housing, especially for Afghan refugees. There is a need to incorporate
social housing into the policy to assist people who cannot afford to
have their own houses.
Abstract: We investigate the large scale of networks in the
context of network survivability under attack. We use appropriate
techniques to evaluate and the attacker-based- and the defenderbased-
network survivability. The attacker is unaware of the operated
links by the defender. Each attacked link has some pre-specified
probability to be disconnected. The defender choice is so that to
maximize the chance of successfully sending the flow to the
destination node. The attacker however will select the cut-set with
the highest chance to be disabled in order to partition the network.
Moreover, we extend the problem to the case of selecting the best p
paths to operate by the defender and the best k cut-sets to target by
the attacker, for arbitrary integers p,k>1. We investigate some
variations of the problem and suggest polynomial-time solutions.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: The acceptance of sustainable products by the final
consumer is still one of the challenges of the industry, which
constantly seeks alternative approaches to successfully be accepted in
the global market. A large set of methods and approaches have been
discussed and analysed throughout the literature. Considering the current need for sustainable development and the
current pace of consumption, the need for a combined solution
towards the development of new products became clear, forcing
researchers in product development to propose alternatives to the
previous standard product development models. This paper presents, through a systemic analysis of the literature
on product development, eco-design and consumer involvement, a set
of alternatives regarding consumer involvement towards the
development of sustainable products and how these approaches could
help improve the sustainable industry’s establishment in the general
market. Still being developed in the course of the author’s PhD, the initial
findings of the research show that the understanding of the benefits of
sustainable behaviour lead to a more conscious acquisition and
eventually to the implementation of sustainable change in the
consumer. Thus this paper is the initial approach towards the
development of new sustainable products using the fashion industry
as an example of practical implementation and acceptance by the
consumers. By comparing the existing literature and critically analysing it, this
paper concluded that the consumer involvement is strategic to
improve the general understanding of sustainability and its features.
The use of consumers and communities has been studied since the
early 90s in order to exemplify uses and to guarantee a fast
comprehension. The analysis done also includes the importance of
this approach for the increase of innovation and ground breaking
developments, thus requiring further research and practical
implementation in order to better understand the implications and
limitations of this methodology.
Abstract: Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
Abstract: The statistical study has become indispensable for various fields of knowledge. Not any different, in Geotechnics the study of probabilistic and statistical methods has gained power considering its use in characterizing the uncertainties inherent in soil properties. One of the situations where engineers are constantly faced is the definition of a probability distribution that represents significantly the sampled data. To be able to discard bad distributions, goodness-of-fit tests are necessary. In this paper, three non-parametric goodness-of-fit tests are applied to a data set computationally generated to test the goodness-of-fit of them to a series of known distributions. It is shown that the use of normal distribution does not always provide satisfactory results regarding physical and behavioral representation of the modeled parameters.
Abstract: In this study, Brillouin Gain Spectrum (BGS) is
experimentally analyzed in the Brillouin Optical Time Domain
Reflectometry (BOTDR) and Brillouin Optical Time Domain
Analyzer (BOTDA). For this purpose, the signal level of the
microwave generator is varied and the effects of BGS are
investigated. In the setups, 20 km conventional single mode fiber is
used to both setups and laser wavelengths are selected around 1550
nm. To achieve best results, it can be used between 5 dBm to 15 dBm
signal level of microwave generator for BOTDA and BOTDR setups.
Abstract: This paper focuses on the study of DC-to-DC
converters, which are suitable for low-voltage high-power
applications. The output voltages generated by renewable energy
sources such as photovoltaic arrays and fuel cell stacks are
generally low and required to be increased to high voltage levels.
Development of DC-to-DC converters, which provide high step-up
voltage conversion ratios with high efficiencies and low voltage
stresses, is one of the main issues in the development of renewable
energy systems. A procedure for three converters−conventional
DC-to-DC converter, interleaved boost converter, and isolated flyback
based converter, is illustrated for a given set of specifications. The
selection among the converters for the given application is based on
the voltage conversion ratio, efficiency, and voltage stresses.
Abstract: This paper sets out a behavioral macro-model of a
Merged PiN and Schottky (MPS) diode based on silicon carbide
(SiC). This model holds good for both static and dynamic electrothermal
simulations for industrial applications. Its parameters have
been worked out from datasheets curves by drawing on the
optimization method: Simulated Annealing (SA) for the SiC MPS
diodes made available in the industry. The model also adopts the
Analog Behavioral Model (ABM) of PSPICE in which it has been
implemented. The thermal behavior of the devices was also taken
into consideration by making use of Foster’ canonical network as
figured out from electro-thermal measurement provided by the
manufacturer of the device.
Abstract: Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
Abstract: Companies face increasing challenges in research due
to higher costs and risks. The intensifying technology complexity and
interdisciplinarity require unique know-how. Therefore, companies
need to decide whether research shall be conducted internally or
externally with partners. On the other hand, research institutes meet
increasing efforts to achieve good financing and to maintain high
research reputation. Therefore, relevant research topics need to be
identified and specialization of competency is necessary. However,
additional competences for solving interdisciplinary research projects
are also often required. Secured financing can be achieved by
bonding industry partners as well as public fundings. The realization
of faster and better research drives companies and research institutes
to cooperate in organized research networks, which are managed by
an administrative organization. For an effective and efficient
cooperation, necessary processes, roles, tools and a set of rules need
to be determined. Goal of this paper is to show the state-of-art
research and to propose a governance framework for organized
research networks.
Abstract: River Hindon is an important river catering the
demand of highly populated rural and industrial cluster of western
Uttar Pradesh, India. Water quality of river Hindon is deteriorating at
an alarming rate due to various industrial, municipal and agricultural
activities. The present study aimed at identifying the pollution
sources and quantifying the degree to which these sources are
responsible for the deteriorating water quality of the river. Various
water quality parameters, like pH, temperature, electrical
conductivity, total dissolved solids, total hardness, calcium, chloride,
nitrate, sulphate, biological oxygen demand, chemical oxygen
demand, and total alkalinity were assessed. Water quality data
obtained from eight study sites for one year has been subjected to the
two multivariate techniques, namely, principal component analysis
and cluster analysis. Principal component analysis was applied with
the aim to find out spatial variability and to identify the sources
responsible for the water quality of the river. Three Varifactors were
obtained after varimax rotation of initial principal components using
principal component analysis. Cluster analysis was carried out to
classify sampling stations of certain similarity, which grouped eight
different sites into two clusters. The study reveals that the
anthropogenic influence (municipal, industrial, waste water and
agricultural runoff) was the major source of river water pollution.
Thus, this study illustrates the utility of multivariate statistical
techniques for analysis and elucidation of multifaceted data sets,
recognition of pollution sources/factors and understanding
temporal/spatial variations in water quality for effective river water
quality management.
Abstract: In the present work, forming limit diagrams and strain
distribution profile diagrams for extra deep drawing steel at room and
elevated temperatures have been determined experimentally by
conducting stretch forming experiments by using designed and
fabricated warm stretchforming tooling setup. With the help of
forming Limit Diagrams (FLDs) and strain, distribution profile
diagrams the formability of Extra Deep Drawing steel has been
analyzed and co-related with mechanical properties like strain
hardening COEFFICIENT (n) and normal anisotropy (r−). Mechanical
properties of EDD steel from room temperature to 4500C were
determined and discussed the impact of temperature on the properties
like work hardening exponent (n) anisotropy (r-) and strength
coefficient of the material. In addition, the fractured surfaces after
stretching have undergone the some metallurgical investigations and
attempt has been made to co-relate with the formability of EDD steel
sheets. They are co-related and good agreement with FLDs at various
temperatures.
Abstract: The aim of this paper is to introduce the notion of
intuitionistic fuzzy positive implicative ideals with thresholds (λ, μ) of
BCI-algebras and to investigate its properties and characterizations.