Abstract: The paper contains an investigation of winding numbers
of paths of zeros of analytic theta functions. We have considered
briefly an analytic representation of finite quantum systems ZN.
The analytic functions on a torus have exactly N zeros. The brief
introduction to the zeros of analytic functions and there time evolution
is given. We have discussed the periodic finite quantum systems. We
have introduced the winding numbers in general. We consider the
winding numbers of the zeros of analytic theta functions.
Abstract: The paper contains an investigation on basic problems
about the zeros of analytic theta functions. A brief introduction to
analytic representation of finite quantum systems is given. The zeros
of this function and there evolution time are discussed. Two open
problems are introduced. The first problem discusses the cases when
the zeros follow the same path. As the basis change the quantum state
|f transforms into different quantum state. The second problem is
to define a map between two toruses where the domain and the range
of this map are the analytic functions on toruses.
Abstract: The aim of our work is to study phase composition,
particle size and magnetic response of Fe2O3/TiO2 nanocomposites
with respect to the final annealing temperature. Those nanomaterials
are considered as smart catalysts, separable from a liquid/gaseous
phase by applied magnetic field. The starting product was obtained
by an ecologically acceptable route, based on heterogeneous
precipitation of the TiO2 on modified g-Fe2O3 nanocrystals dispersed
in water. The precursor was subsequently annealed on air at
temperatures ranging from 200 oC to 900 oC. The samples were
investigated by synchrotron X-ray powder diffraction (S-PXRD),
magnetic measurements and Mössbauer spectroscopy. As evidenced
by S-PXRD and Mössbauer spectroscopy, increasing the annealing
temperature causes evolution of the phase composition from
anatase/maghemite to rutile/hematite, finally above 700 oC the
pseudobrookite (Fe2TiO5) also forms. The apparent particle size of
the various Fe2O3/TiO2 phases has been determined from the highquality
S-PXRD data by using two different approaches: the Rietveld
refinement and the Debye method. Magnetic response of the samples
is discussed in considering the phase composition and the particle
size.
Abstract: When reconstructing a scenario, it is necessary to
know the structure of the elements present on the scene to have an
interpretation. In this work we link 3D scenes reconstruction to
evolutionary algorithms through the vision stereo theory. We
consider vision stereo as a method that provides the reconstruction of
a scene using only a couple of images of the scene and performing
some computation. Through several images of a scene, captured from
different positions, vision stereo can give us an idea about the threedimensional
characteristics of the world. Vision stereo usually
requires of two cameras, making an analogy to the mammalian vision
system. In this work we employ only a camera, which is translated
along a path, capturing images every certain distance. As we can not
perform all computations required for an exhaustive reconstruction,
we employ an evolutionary algorithm to partially reconstruct the
scene in real time. The algorithm employed is the fly algorithm,
which employ “flies" to reconstruct the principal characteristics of
the world following certain evolutionary rules.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: Planning the transition period for the adoption of
alternative fuel-technology powertrains is a challenging task that
requires sophisticated analysis tools. In this study, a system dynamic
approach was applied to analyze the bi-directional interaction
between the development of the refueling station network and vehicle
sales. Besides, the developed model was used to estimate the
transition cost to reach a predefined target (share of alternative fuel
vehicles) in different scenarios. Several scenarios have been analyzed
to investigate the effectiveness and cost of incentives on the initial
price of vehicles, and on the evolution of fuel and refueling stations.
Obtained results show that a combined set of incentives will be more
effective than just a single specific type of incentives.
Abstract: In this research paper we have presented control
architecture for robotic arm movement and trajectory planning using
Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is
used to compensate the uncertainties like; movement, friction and
settling time in robotic arm movement. The genetic algorithms and
fuzzy logic is used to meet the objective of optimal control
movement of robotic arm. This proposed technique represents a
general model for redundant structures and may extend to other
structures. Results show optimal angular movement of joints as result
of evolutionary process. This technique has edge over the other
techniques as minimum mathematics complexity used.
Abstract: A systems approach model for prostate cancer in prostate duct, as a sub-system of the organism is developed. It is accomplished in two steps. First this research work starts with a nonlinear system of coupled Fokker-Plank equations which models continuous process of the system like motion of cells. Then extended to PDEs that include discontinuous processes like cell mutations, proliferation and deaths. The discontinuous processes is modeled by using intensity poisson processes. The model incorporates the features of the prostate duct. The system of PDEs spatial coordinate is along the proximal distal axis. Its parameters depend on features of the prostate duct. The movement of cells is biased towards distal region and mutations of prostate cancer cells is localized in the proximal region. Numerical solutions of the full system of equations are provided, and are exhibit traveling wave fronts phenomena. This motivates the use of the standard transformation to derive a canonically related system of ODEs for traveling wave solutions. The results obtained show persistence of prostate cancer by showing that the non-negative cone for the traveling wave system is time invariant. The traveling waves have a unique global attractor is proved also. Biologically, the global attractor verifies that evolution of prostate cancer stem cells exhibit the avascular tumor growth. These numerical solutions show that altering prostate stem cell movement or mutation of prostate cancer cells lead to avascular tumor. Conclusion with comments on clinical implications of the model is discussed.
Abstract: This paper presents a means for reducing the torque
variation during the revolution of a vertical-axis water turbine
(VAWaterT) by increasing the blade number. For this purpose, twodimensional
CFD analyses have been performed on a straight-bladed
Darrieus-type rotor. After describing the computational model and
the relative validation procedure, a complete campaign of
simulations, based on full RANS unsteady calculations, is proposed
for a three, four and five-bladed rotor architectures, characterized by
a NACA 0025 airfoil. For each proposed rotor configuration, flow
field characteristics are investigated at several values of tip speed
ratio, allowing a quantification of the influence of blade number on
flow geometric features and dynamic quantities, such as rotor torque
and power. Finally, torque and power curves are compared for the
three analyzed architectures, achieving a quantification of the effect
of blade number on overall rotor performance.
Abstract: This paper demonstrates the application of craziness based particle swarm optimization (CRPSO) technique for designing the 8th order low pass Infinite Impulse Response (IIR) filter. CRPSO, the much improved version of PSO, is a population based global heuristic search algorithm which finds near optimal solution in terms of a set of filter coefficients. Effectiveness of this algorithm is justified with a comparative study of some well established algorithms, namely, real coded genetic algorithm (RGA) and particle swarm optimization (PSO). Simulation results affirm that the proposed algorithm CRPSO, outperforms over its counterparts not only in terms of quality output i.e. sharpness at cut-off, pass band ripple, stop band ripple, and stop band attenuation but also in convergence speed with assured stability.
Abstract: The present models and simulation algorithms of intracellular stochastic kinetics are usually based on the premise that diffusion is so fast that the concentrations of all the involved species are homogeneous in space. However, recents experimental measurements of intracellular diffusion constants indicate that the assumption of a homogeneous well-stirred cytosol is not necessarily valid even for small prokaryotic cells. In this work a mathematical treatment of diffusion that can be incorporated in a stochastic algorithm simulating the dynamics of a reaction-diffusion system is presented. The movement of a molecule A from a region i to a region j of the space is represented as a first order reaction Ai k- ! Aj , where the rate constant k depends on the diffusion coefficient. The diffusion coefficients are modeled as function of the local concentration of the solutes, their intrinsic viscosities, their frictional coefficients and the temperature of the system. The stochastic time evolution of the system is given by the occurrence of diffusion events and chemical reaction events. At each time step an event (reaction or diffusion) is selected from a probability distribution of waiting times determined by the intrinsic reaction kinetics and diffusion dynamics. To demonstrate the method the simulation results of the reaction-diffusion system of chaperoneassisted protein folding in cytoplasm are shown.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: The article emphasizes the ideological commitment of
the philosopher Emil Cioran. It presents firstly Cioran's works on the
theme announced by the title, then the European context that
determined the political option of Cioran and a brief analysis of his
relationship with History during his French period. The anti-
Semitism of Cioran was favored by his attachment to a few
philosophers, but also by the European extremist and anti-Semitic
context. The article seeks to demonstrate that the philosopher Cioran,
known more for his pessimism and nihilism, maintained in time an
obsessive relationship with History. His political philosophy is as
important as his subjective philosophy, better known than the former.
Abstract: Petrology and geochemical characteristics of granitic
rocks from South Sulawesi, especially from Polewaliand Masamba
area are presented in order to elucidate their origin of magma and
geodynamic setting. The granitic rocks in these areas are dominated by
granodiorite and granite in composition. Quartz, K-feldspar and
plagioclase occur as major phases with hornblende and biotite as
major ferromagnesian minerals. All of the samples were plotted in
calc-alkaline field, show metaluminous affinity and typical of I-type
granitic rock. Harker diagram indicates that granitic rocks experienced
fractional crystallization during magmatic evolution. Both groups
displayed an extreme enrichment of LILE, LREE and a slight negative
Eu anomaly which resemble upper continental crust affinity. They
were produced from partial melting of upper continental crust and
have close relationship of sources composition within a suite. The
geochemical characteristics explained the arc related subduction
environment which later give an evidence of continent-continent
collision between Australia-derived microcontinent and Sundalandto
form continental arc environment.
Abstract: New exact three-wave solutions including periodic two-solitary solutions and doubly periodic solitary solutions for the (2+1)-dimensional asymmetric Nizhnik-Novikov- Veselov (ANNV) system are obtained using Hirota's bilinear form and generalized three-wave type of ansatz approach. It is shown that the generalized three-wave method, with the help of symbolic computation, provides an e¤ective and powerful mathematical tool for solving high dimensional nonlinear evolution equations in mathematical physics.
Abstract: Nature conducts its action in a very private manner. To
reveal these actions classical science has done a great effort. But
classical science can experiment only with the things that can be seen
with eyes. Beyond the scope of classical science quantum science
works very well. It is based on some postulates like qubit,
superposition of two states, entanglement, measurement and
evolution of states that are briefly described in the present paper.
One of the applications of quantum computing i.e.
implementation of a novel quantum evolutionary algorithm(QEA) to
automate the time tabling problem of Dayalbagh Educational Institute
(Deemed University) is also presented in this paper. Making a good
timetable is a scheduling problem. It is NP-hard, multi-constrained,
complex and a combinatorial optimization problem. The solution of
this problem cannot be obtained in polynomial time. The QEA uses
genetic operators on the Q-bit as well as updating operator of
quantum gate which is introduced as a variation operator to converge
toward better solutions.
Abstract: In this paper, we propose a single sample path based
algorithm with state aggregation to optimize the average rewards of
singularly perturbed Markov reward processes (SPMRPs) with a
large scale state spaces. It is assumed that such a reward process
depend on a set of parameters. Differing from the other kinds of
Markov chain, SPMRPs have their own hierarchical structure. Based
on this special structure, our algorithm can alleviate the load in the
optimization for performance. Moreover, our method can be applied
on line because of its evolution with the sample path simulated.
Compared with the original algorithm applied on these problems of
general MRPs, a new gradient formula for average reward
performance metric in SPMRPs is brought in, which will be proved
in Appendix, and then based on these gradients, the schedule of the
iteration algorithm is presented, which is based on a single sample
path, and eventually a special case in which parameters only
dominate the disturbance matrices will be analyzed, and a precise
comparison with be displayed between our algorithm with the old
ones which is aim to solve these problems in general Markov reward
processes. When applied in SPMRPs, our method will approach a fast
pace in these cases. Furthermore, to illustrate the practical value of
SPMRPs, a simple example in multiple programming in computer
systems will be listed and simulated. Corresponding to some practical
model, physical meanings of SPMRPs in networks of queues will be
clarified.