Abstract: The objective of the present study was to evaluate the
potential of hollow microneedles for enhancing the transdermal
delivery of Bovine Serum Albumin (MW~66,000 Da)-Fluorescein
Isothiocyanate (BSA-FITC) conjugate, a hydrophilic large molecular
compound. Moreover, the effect of different formulations was
evaluated. The series of binary mixtures composed of propylene
glycol (PG) and pH 7.4 phosphate buffer solution (PBS) was
prepared and used as a medium for BSA-FITC. The results showed
that there was no permeation of BSA-FITC solution across the
neonatal porcine skin without using hollow microneedles, whereas
the cumulative amount of BSA-FITC released at 8 h through the
neonatal porcine skin was about 60-70% when using hollow
microneedles. Furthermore, the results demonstrated that the higher
volume of PG in binary mixtures injected, the lower cumulative
amount of BSA-FITC released and release rate of BSA-FITC from
skin. These release profiles of BSA-FITC in binary mixtures were
expressed by Fick-s law of diffusion. These results suggest the
utilization of hollow microneedle to enhance transdermal delivery of
protein and provide useful information for designing an effective
hollow microneedle system.
Abstract: Finite impulse response (FIR) filters have the advantage of linear phase, guaranteed stability, fewer finite precision errors, and efficient implementation. In contrast, they have a major disadvantage of high order need (more coefficients) than IIR counterpart with comparable performance. The high order demand imposes more hardware requirements, arithmetic operations, area usage, and power consumption when designing and fabricating the filter. Therefore, minimizing or reducing these parameters, is a major goal or target in digital filter design task. This paper presents an algorithm proposed for modifying values and the number of non-zero coefficients used to represent the FIR digital pulse shaping filter response. With this algorithm, the FIR filter frequency and phase response can be represented with a minimum number of non-zero coefficients. Therefore, reducing the arithmetic complexity needed to get the filter output. Consequently, the system characteristic i.e. power consumption, area usage, and processing time are also reduced. The proposed algorithm is more powerful when integrated with multiplierless algorithms such as distributed arithmetic (DA) in designing high order digital FIR filters. Here the DA usage eliminates the need for multipliers when implementing the multiply and accumulate unit (MAC) and the proposed algorithm will reduce the number of adders and addition operations needed through the minimization of the non-zero values coefficients to get the filter output.
Abstract: Prime Factorization based on Quantum approach in
two phases has been performed. The first phase has been achieved at
Quantum computer and the second phase has been achieved at the
classic computer (Post Processing). At the second phase the goal is to
estimate the period r of equation xrN ≡ 1 and to find the prime factors
of the composite integer N in classic computer. In this paper we
present a method based on Randomized Approach for estimation the
period r with a satisfactory probability and the composite integer N
will be factorized therefore with the Randomized Approach even the
gesture of the period is not exactly the real period at least we can find
one of the prime factors of composite N. Finally we present some
important points for designing an Emulator for Quantum Computer
Simulation.
Abstract: The present work is concerned with the effect of turning process parameters (cutting speed, feed rate, and depth of cut) and distance from the center of work piece as input variables on the chip micro-hardness as response or output. Three experiments were conducted; they were used to investigate the chip micro-hardness behavior at diameter of work piece for 30[mm], 40[mm], and 50[mm]. Response surface methodology (R.S.M) is used to determine and present the cause and effect of the relationship between true mean response and input control variables influencing the response as a two or three dimensional hyper surface. R.S.M has been used for designing a three factor with five level central composite rotatable factors design in order to construct statistical models capable of accurate prediction of responses. The results obtained showed that the application of R.S.M can predict the effect of machining parameters on chip micro-hardness. The five level factorial designs can be employed easily for developing statistical models to predict chip micro-hardness by controllable machining parameters. Results obtained showed that the combined effect of cutting speed at it?s lower level, feed rate and depth of cut at their higher values, and larger work piece diameter can result increasing chi micro-hardness.
Abstract: Chemical detection is still a continuous challenge when
it comes to designing single-walled carbon nanotube (SWCNT)
sensors with high selectivity, especially in complex chemical
environments. A perfect example of such an environment would be in
thermally oxidized soybean oil. At elevated temperatures, oil oxidizes
through a series of chemical reactions which results in the formation of
monoacylglycerols, diacylglycerols, oxidized triacylglycerols, dimers,
trimers, polymers, free fatty acids, ketones, aldehydes, alcohols,
esters, and other minor products. In order to detect the rancidity of
oxidized soybean oil, carbon nanotube chemiresistor sensors have
been coated with polyethylenimine (PEI) to enhance the sensitivity
and selectivity. PEI functionalized SWCNTs are known to have a high
selectivity towards strong electron withdrawing molecules. The
sensors were very responsive to different oil oxidation levels and
furthermore, displayed a rapid recovery in ambient air without the
need of heating or UV exposure.
Abstract: Nowadays, the earth is countered with serious problem
of air pollution. This problem has been started from the industrial
revolution and has been faster in recent years, so that leads the earth
to ecological and environmental disaster. One of its results is the
global warming problem and its related increase in global
temperature. The most important factors in air pollution especially in
urban environments are Automobiles and residential buildings that are
the biggest consumers of the fossil energies, so that if the residential
buildings as a big part of the consumers of such energies reduce their
consumption rate, the air pollution will be decreased. Since
Metropolises are the main centers of air pollution in the world,
assessment and analysis of efficient strategies in decreasing air
pollution in such cities, can lead to the desirable and suitable results
and can solve the problem at least in critical level. Tabriz city is one
of the most important metropolises in North west of Iran that about
two million people are living there. for its situation in cold dry
climate, has a high rate of fossil energies consumption that make air
pollution in its urban environment. These two factors, being both
metropolis and in cold dry climate, make this article try to analyze the
strategies of climatic design in old districts of the city and use them in
new districts of the future. These strategies can be used in this city
and other similar cities and pave the way to reduce energy
consumption and related air pollution to save whole world.
Abstract: In the real application of active control systems to
mitigate the response of structures subjected to sever external
excitations such as earthquake and wind induced vibrations, since the
capacity of actuators is limited then the actuators saturate. Hence, in
designing controllers for linear and nonlinear structures under sever
earthquakes, the actuator saturation should be considered as a
constraint. In this paper optimal design of active controllers for
nonlinear structures by considering the actuator saturation has been
studied. To this end a method has been proposed based on defining
an optimization problem which considers the minimizing of the
maximum displacement of the structure as objective when a limited
capacity for actuator has been used as a constraint in optimization
problem. To evaluate the effectiveness of the proposed method, a
single degree of freedom (SDF) structure with a bilinear hysteretic
behavior has been simulated under a white noise ground acceleration
of different amplitudes. Active tendon control mechanism, comprised
of pre-stressed tendons and an actuator, and extended nonlinear
Newmark method based instantaneous optimal control algorithm
have been used as active control mechanism and algorithm. To
enhance the efficiency of the controllers, the weights corresponding
to displacement, velocity, acceleration and control force in the
performance index have been found by using the Distributed Genetic
Algorithm (DGA). According to the results it has been concluded
that the proposed method has been effective in considering the
actuator saturation in designing optimal controllers for nonlinear
frames. Also it has been shown that the actuator capacity and the
average value of required control force are two important factors in
designing nonlinear controllers for considering the actuator
saturation.
Abstract: Due to the non- intuitive nature of Quantum
algorithms, it becomes difficult for a classically trained person to
efficiently construct new ones. So rather than designing new
algorithms manually, lately, Genetic algorithms (GA) are being
implemented for this purpose. GA is a technique to automatically
solve a problem using principles of Darwinian evolution. This has
been implemented to explore the possibility of evolving an n-qubit
circuit when the circuit matrix has been provided using a set of
single, two and three qubit gates. Using a variable length population
and universal stochastic selection procedure, a number of possible
solution circuits, with different number of gates can be obtained for
the same input matrix during different runs of GA. The given
algorithm has also been successfully implemented to obtain two and
three qubit Boolean circuits using Quantum gates. The results
demonstrate the effectiveness of the GA procedure even when the
search spaces are large.
Abstract: Nowadays over-consumption of fossil energy in
buildings especially in residential buildings and also considering the
increase in populations, the crisis of energy shortage in a near future
is predictable. The recent performance of developed countries in
construction with the aim of decreasing fossil energies shows that
these countries have understood the incoming crisis and has taken
reasonable and basic actions in this regard. However, Iranian
architecture, with several thousands years of history, has acquired
and executed invaluable experiences in designing, adapting and
coordinating with the nature.
Architectural studies during the recent decades show that imitating
modern western architecture results in high energy wastage beside
the fact that it not reasonably adaptable and corresponded with the
habits and customs of people unlike the architecture in the past which
was compatible and adaptable with the climatic conditions and this
necessitates optimal using of renewable energies more than ever. This
paper studies problems of design, execution and living in today's
houses and reviews the characteristics of climatic elements paying
special attention to the performance of trombe wall and solar
greenhouse in traditional houses and offers some suggestions for
combining these two elements and a climatic strategy.
Abstract: Designing a simulated system and training it to optimize its tasks in simulated environment helps the designers to avoid problems that may appear when designing the system directly in real world. These problems are: time consuming, high cost, high errors percentage and low efficiency and accuracy of the system. The proposed system will investigate and improve the efficiency and accuracy of a simulated robot to choose correct behavior to perform its task. In this paper, machine learning, which uses genetic algorithm, is adopted. This type of machine learning is called genetic-based machine learning in which a distributed classifier system is used to improve the efficiency and accuracy of the robot. Consequently, it helps the robot to achieve optimal action.
Abstract: The purpose of this paper is to shed light on the
controversial subject of tax incentives to promote regional
development. Although extensive research has been conducted, a
review of the literature gives an inconclusive answer to whether
economic incentives are effective. One reason is the fact that for
some researchers “effective" means the significant location of new
firms in targeted areas, while for others the creation of jobs
regardless if new firms are arriving in a significant fashion. We
present this dichotomy by analyzing a tax incentive program via both
alternatives: location and job creation. The contribution of the paper
is to inform policymakers about the potential opportunities and
pitfalls when designing incentive strategies. This is particularly
relevant, given that both the US and Europe have been promoting
incentives as a tool for regional economic development.
Abstract: The analysis of electromagnetic environment using
deterministic mathematical models is characterized by the
impossibility of analyzing a large number of interacting network
stations with a priori unknown parameters, and this is characteristic,
for example, of mobile wireless communication networks. One of the
tasks of the tools used in designing, planning and optimization of
mobile wireless network is to carry out simulation of electromagnetic
environment based on mathematical modelling methods, including
computer experiment, and to estimate its effect on radio
communication devices. This paper proposes the development of a
statistical model of electromagnetic environment of a mobile
wireless communication network by describing the parameters and
factors affecting it including the propagation channel and their
statistical models.
Abstract: Although Model Driven Architecture has taken
successful steps toward model-based software development, this
approach still faces complex situations and ambiguous questions
while applying to real world software systems. One of these
questions - which has taken the most interest and focus - is how
model transforms between different abstraction levels, MDA
proposes. In this paper, we propose an approach based on Story
Driven Modeling and Aspect Oriented Programming to ease these
transformations. Service Oriented Architecture is taken as the target
model to test the proposed mechanism in a functional system.
Service Oriented Architecture and Model Driven Architecture [1]
are both considered as the frontiers of their own domain in the
software world. Following components - which was the greatest step
after object oriented - SOA is introduced, focusing on more
integrated and automated software solutions. On the other hand - and
from the designers' point of view - MDA is just initiating another
evolution. MDA is considered as the next big step after UML in
designing domain.
Abstract: This paper covers the present situation and problem of experimental teaching of mathematics specialty in recent years, puts
forward and demonstrates experimental teaching methods for different
education. From the aspects of content and experimental teaching
approach, uses as an example the course “Experiment for Program
Designing & Algorithmic Language" and discusses teaching practice
and laboratory course work. In addition a series of successful methods
and measures are introduced in experimental teaching.
Abstract: In this paper, various algorithms for designing quadrature mirror filter are reviewed and a new algorithm is presented for the design of near perfect reconstruction quadrature mirror filter bank. In the proposed algorithm, objective function is formulated using the perfect reconstruction condition or magnitude response condition of prototype filter at frequency (ω = 0.5π) in ideal condition. The cutoff frequency is iteratively changed to adjust the filters coefficients using optimization algorithm. The performances of the proposed algorithm are evaluated in term of computation time, reconstruction error and number of iterations. The design examples illustrate that the proposed algorithm is superior in term of peak reconstruction error, computation time, and number of iterations. The proposed algorithm is simple, easy to implement, and linear in nature.
Abstract: Privacy issues commonly discussed among
researchers, practitioners, and end-users in pervasive healthcare.
Pervasive healthcare systems are applications that can support
patient-s need anytime and anywhere. However, pervasive healthcare
raises privacy concerns since it can lead to situations where patients
may not be aware that their private information is being shared and
becomes vulnerable to threat. We have systematically analyzed the
privacy issues and present a summary in tabular form to show the
relationship among the issues. The six issues identified are medical
information misuse, prescription leakage, medical information
eavesdropping, social implications for the patient, patient difficulties
in managing privacy settings, and lack of support in designing
privacy-sensitive applications. We narrow down the issues and chose
to focus on the issue of 'lack of support in designing privacysensitive
applications' by proposing a privacy-sensitive architecture
specifically designed for pervasive healthcare monitoring systems.
Abstract: An Optimal Power Flow based on Improved Particle
Swarm Optimization (OPF-IPSO) with Generator Capability Curve
Constraint is used by NN-OPF as a reference to get pattern of
generator scheduling. There are three stages in Designing NN-OPF.
The first stage is design of OPF-IPSO with generator capability curve
constraint. The second stage is clustering load to specific range and
calculating its index. The third stage is training NN-OPF using
constructive back propagation method. In training process total load
and load index used as input, and pattern of generator scheduling
used as output. Data used in this paper is power system of Java-Bali.
Software used in this simulation is MATLAB.
Abstract: In this paper, a mathematical model of human immunodeficiency
virus (HIV) is utilized and an optimization problem is
proposed, with the final goal of implementing an optimal 900-day
structured treatment interruption (STI) protocol. Two type of commonly
used drugs in highly active antiretroviral therapy (HAART),
reverse transcriptase inhibitors (RTI) and protease inhibitors (PI), are
considered. In order to solving the proposed optimization problem an
adaptive memetic algorithm with population management (AMAPM)
is proposed. The AMAPM uses a distance measure to control the
diversity of population in genotype space and thus preventing the
stagnation and premature convergence. Moreover, the AMAPM uses
diversity parameter in phenotype space to dynamically set the population
size and the number of crossovers during the search process.
Three crossover operators diversify the population, simultaneously.
The progresses of crossover operators are utilized to set the number
of each crossover per generation. In order to escaping the local optima
and introducing the new search directions toward the global optima,
two local searchers assist the evolutionary process. In contrast to
traditional memetic algorithms, the activation of these local searchers
is not random and depends on both the diversity parameters in
genotype space and phenotype space. The capability of AMAPM in
finding optimal solutions compared with three popular metaheurestics
is introduced.
Abstract: Developing a stable early warning system (EWS)
model that is capable to give an accurate prediction is a challenging
task. This paper introduces k-nearest neighbour (k-NN) method
which never been applied in predicting currency crisis before with the
aim of increasing the prediction accuracy. The proposed k-NN
performance depends on the choice of a distance that is used where in
our analysis; we take the Euclidean distance and the Manhattan as a
consideration. For the comparison, we employ three other methods
which are logistic regression analysis (logit), back-propagation neural
network (NN) and sequential minimal optimization (SMO). The
analysis using datasets from 8 countries and 13 macro-economic
indicators for each country shows that the proposed k-NN method
with k = 4 and Manhattan distance performs better than the other
methods.