Abstract: We introduce a new model called the Marshall-Olkin Rayleigh distribution which extends the Rayleigh distribution using Marshall-Olkin transformation and has increasing and decreasing shapes for the hazard rate function. Various structural properties of the new distribution are derived including explicit expressions for the moments, generating and quantile function, some entropy measures, and order statistics are presented. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The potentiality of the new model is illustrated by means of a simulation study.
Abstract: This paper considers the design of Dual Proportional-
Integral (DPI) Load Frequency Control (LFC), using gravitational
search algorithm (GSA). The design is carried out for nonlinear
hydrothermal power system where generation rate constraint (GRC)
and governor dead band are considered. Furthermore, time delays
imposed by governor-turbine, thermodynamic process, and
communication channels are investigated. GSA is utilized to search
for optimal controller parameters by minimizing a time-domain based
objective function. GSA-based DPI has been compared to Ziegler-
Nichols based PI, and Genetic Algorithm (GA) based PI controllers
in order to demonstrate the superior efficiency of the proposed
design. Simulation results are carried for a wide range of operating
conditions and system parameters variations.
Abstract: This paper introduces a method to optimal design of a
hybrid Wind/Photovoltaic/Fuel cell generation system for a typical
domestic load that is not located near the electricity grid. In this
configuration the combination of a battery, an electrolyser, and a
hydrogen storage tank are used as the energy storage system. The aim
of this design is minimization of overall cost of generation scheme
over 20 years of operation. The Matlab/Simulink is applied for
choosing the appropriate structure and the optimization of system
sizing. A teaching learning based optimization is used to optimize the
cost function. An overall power management strategy is designed for
the proposed system to manage power flows among the different
energy sources and the storage unit in the system. The results have
been analyzed in terms of technical and economic. The simulation
results indicate that the proposed hybrid system would be a feasible
solution for stand-alone applications at remote locations.
Abstract: We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
multiple scales.
Abstract: The paper deals with the classical fiber bundle model
of equal load sharing, sometimes referred to as the Daniels’ bundle
or the democratic bundle. Daniels formulated a multidimensional
integral and also a recursive formula for evaluation of the
strength cumulative distribution function. This paper describes
three algorithms for evaluation of the recursive formula and also
their implementations with source codes in the Python high-level
programming language. A comparison of the algorithms are provided
with respect to execution time. Analysis of orders of magnitudes of
addends in the recursion is also provided.
Abstract: Microscopic simulation tool kits allow for
consideration of the two processes of railway operations and the
previous timetable production. Block occupation conflicts on both
process levels are often solved by using defined train priorities. These
conflict resolutions (dispatching decisions) generate reactionary
delays to the involved trains. The sum of reactionary delays is
commonly used to evaluate the quality of railway operations, which
describes the timetable robustness. It is either compared to an
acceptable train performance or the delays are appraised
economically by linear monetary functions. It is impossible to
adequately evaluate dispatching decisions without a well-founded
objective function. This paper presents a new approach for the
evaluation of dispatching decisions. The approach uses mode choice
models and considers the behaviour of the end-customers. These
models evaluate the reactionary delays in more detail and consider
other competing modes of transport. The new approach pursues the
coupling of a microscopic model of railway operations with the
macroscopic choice mode model. At first, it will be implemented for
railway operations process but it can also be used for timetable
production. The evaluation considers the possibility for the customer
to interchange to other transport modes. The new approach starts to
look at rail and road, but it can also be extended to air travel. The
result of mode choice models is the modal split. The reactions by the
end-customers have an impact on the revenue of the train operating
companies. Different purposes of travel have different payment
reserves and tolerances towards late running. Aside from changes to
revenues, longer journey times can also generate additional costs.
The costs are either time- or track-specific and arise from required
changes to rolling stock or train crew cycles. Only the variable values
are summarised in the contribution margin, which is the base for the
monetary evaluation of delays. The contribution margin is calculated
for different possible solutions to the same conflict. The conflict
resolution is optimised until the monetary loss becomes minimal. The
iterative process therefore determines an optimum conflict resolution
by monitoring the change to the contribution margin. Furthermore, a
monetary value of each dispatching decision can also be derived.
Abstract: This paper is focused on the CFD simulation of the radiaxial pump (i.e. mixed flow pump) with the aim to detect the reasons of Y-Q characteristic instability. The main reasons of pressure pulsations were detected by means of the analysis of velocity and pressure fields within the pump combined with the theoretical approach. Consequently, the modifications of spiral case and pump suction area were made based on the knowledge of flow conditions and the shape of dissipation function. The primary design of pump geometry was created as the base model serving for the comparison of individual modification influences. The basic experimental data are available for this geometry. This approach replaced the more complicated and with respect to convergence of all computational tasks more difficult calculation for the compressible liquid flow. The modification of primary pump consisted in inserting the three fins types. Subsequently, the evaluation of pressure pulsations, specific energy curves and visualization of velocity fields were chosen as the criterion for successful design.
Abstract: An artificial neural network is a mathematical model
inspired by biological neural networks. There are several kinds of
neural networks and they are widely used in many areas, such as:
prediction, detection, and classification. Meanwhile, in day to day life,
people always have to make many difficult decisions. For example,
the coach of a soccer club has to decide which offensive player
to be selected to play in a certain game. This work describes a
novel Neural Network using a combination of the General Regression
Neural Network and the Probabilistic Neural Networks to help a
soccer coach make an informed decision.
Abstract: Neuroplasticity or the flexibility of the neural system
is the ability of the brain to adapt to the lack or deterioration of sense
and the capability of the neural system to modify itself through
changing shape and function. Not only have studies revealed that
neuroplasticity does not end in childhood, but also they have proven
that it continues till the end of life and is not limited to the neural
system and covers the cognitive system as well. In the field of
cognition, neuroplasticity is defined as the ability to change old
thoughts according to new conditions and the individuals' differences
in using various styles of cognitive regulation inducing several social,
emotional and cognitive outcomes. This paper attempts to discuss and
define major theories and principles of neuroplasticity and elaborate
on nature or nurture.
Abstract: In this paper, an explicit homotopic function is
constructed to compute the Hochschild homology of a finite
dimensional free k-module V. Because the polynomial algebra is of
course fundamental in the computation of the Hochschild homology
HH and the cyclic homology CH of commutative algebras, we
concentrate our work to compute HH of the polynomial algebra, by
providing certain homotopic function.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: Doxorubicin, also known as Adriamycin, is an
anthracycline class of drug used in cancer chemotherapy. It is used in
the treatment of non-Hodgkin’s lymphoma, multiple myeloma, acute
leukemia, breast cancer, lung cancer, endometrium cancer and ovary
cancers. It functions via intercalating DNA and ultimately killing
cancer cells. The major side effects of doxorubicin are hair loss,
myelosuppression, nausea & vomiting, oesophagitis, diarrhea, heart
damage and liver dysfunction. The minor modifications in the
structure of compound exhibit large variation in the biological
activity, has prompted us to carry out the synthesis of sulfonamide
derivatives. Sulfonamide is an important feature with broad spectrum
of biological activity such as antiviral, antifungal, diuretics, antiinflammatory,
antibacterial and anticancer activities. Structure of the
synthesized compound N-(1-methyl-2-oxo-2-N-methyl anilinoethyl)
benzene sulfonamide confirmed by proton nuclear magnetic
resonance (1H NMR),13C NMR, Mass and FTIR spectroscopic tools
to assure the position of all protons and hence stereochemistry of the
molecule. Further we have reported the binding potential of
synthesized sulfonamide analogues in comparison to doxorubicin
drug using Auto Dock 4.2 software. Computational binding energy
(B.E.) and inhibitory constant (Ki) has been evaluated for the
synthesized compound in comparison of doxorubicin against Poly
(dA-dT).Poly (dA-dT) and Poly (dG-dC).Poly (dG-dC) sequences.
The in vitro cytotoxic study against human breast cancer cell lines
confirms the better anticancer activity of the synthesized compound
over currently in use anticancer drug doxorubicin. The IC50 value of
the synthesized compound is 7.12 μM whereas for doxorubicin is 7.2
μM.
Abstract: In this letter, we explore exact solutions for the
Horava-Lifshitz gravity. We use of an extension of this theory with
first order dynamical lapse function. The equations of motion have
been derived in a fully consistent scenario. We assume that there
are some spherically symmetric families of exact solutions of this
extended theory of gravity. We obtain exact solutions and investigate
the singularity structures of these solutions. Specially, an exact
solution with the regular horizon is found.
Abstract: A mixed method by combining modified pole
clustering technique and modified cauer continued fraction is
proposed for reducing the order of the large-scale dynamic systems.
The denominator polynomial of the reduced order model is obtained
by using modified pole clustering technique while the coefficients of
the numerator are obtained by modified cauer continued fraction.
This method generated 'k' number of reduced order models for kth
order reduction. The superiority of the proposed method has been
elaborated through numerical example taken from the literature and
compared with few existing order reduction methods.
Abstract: Medical imaging technology has experienced a
dramatic change in the last few years. Medical imaging refers to the
techniques and processes used to create images of the human body
(or parts thereof) for various clinical purposes such as medical
procedures and diagnosis or medical science including the study of
normal anatomy and function. With the growth of computers and
image technology, medical imaging has greatly influenced the
medical field. The diagnosis of a health problem is now highly
dependent on the quality and the credibility of the image analysis.
This paper deals with the various aspects and types of medical
imaging.
Abstract: The parameters of a two-layer soil can be determined by
processing resistivity data obtained from resistivity measurements
carried out on the soil of interest. The processing usually entails
applying the resistivity data as inputs to an optimisation function.
This paper proposes an algorithm which utilises the square error as an
optimisation function. Resistivity data from previous works were
applied to test the accuracy of the new algorithm developed and the
result obtained conforms significantly to results from previous works.
Abstract: Innovations not only contribute to competitiveness of
the company but have also positive effects on revenues. On average,
product innovations account to 14 percent of companies’ sales.
Innovation management has substantially changed during the last
decade, because of growing reliance on external partners. As a
consequence, a new task for purchasing arises, as firms need to
understand which suppliers actually do have high potential
contributing to the innovativeness of the firm and which do not.
Proper organization of the purchasing function is important since
for the majority of manufacturing companies deal with substantial
material costs which pass through the purchasing function. In the past
the purchasing function was largely seen as a transaction-oriented,
clerical function but today purchasing is the intermediate with supply
chain partners contributing to innovations, be it product or process
innovations. Therefore, purchasing function has to be organized
differently to enable firm innovation potential.
However, innovations are inherently risky. There are behavioral
risk (that some partner will take advantage of the other party),
technological risk in terms of complexity of products and processes
of manufacturing and incoming materials and finally market risks,
which in fact judge the value of the innovation. These risks are
investigated in this work. Specifically, technological risks which deal
with complexity of the products, and processes will be investigated
more thoroughly. Buying components or such high edge technologies
necessities careful investigation of technical features and therefore is
usually conducted by a team of experts. Therefore it is hypothesized
that higher the technological risk, higher will be the centralization of
the purchasing function as an interface with other supply chain
members.
Main contribution of this research lies is in the fact that analysis
was performed on a large data set of 1493 companies, from 25
countries collected in the GMRG 4 survey. Most analyses of
purchasing function are done by case study analysis of innovative
firms. Therefore this study contributes with empirical evaluations that
can be generalized.
Abstract: This paper presents circular polar coordinates
transformation of periodic fuzzy membership function. The purpose
is identification of domain of periodic membership functions in
consequent part of IF-THEN rules. Proposed methods in this paper
remove complicatedness concerning domain of periodic membership
function from defuzzification in fuzzy approximate reasoning.
Defuzzification on circular polar coordinates is also proposed.
Abstract: Cognitive symptoms and behavioral symptoms may, in fact, overlap and be related to the level of the general cognitive function. We have measured the behavioral aspects of autism and its correlation to the cognitive ability in 30 children with ASD. We used a neuropsychological Battery CANTAB eclipse to evaluate the ASD children's cognitive ability. Individuals with ASD and challenging behaviors showed significant correlation between some cognitive abilities and Motor aspects. Based on these findings, we can conclude that the motor behavioral problems in autism affect specific cognitive abilities in ASDs such as comprehension, learning, reversal, acquisition, attention set shifting, and speed of reaction to one stimulus. Future researches should also focus on the relationship between motor stereotypes and other subtypes of repetitive behaviors, such as verbal stereotypes, ritual routine adherence, and the use of different types of CANTAB tests.