Abstract: Residual dye contents in textile dyeing wastewater have complex aromatic structures that are resistant to degrade in biological wastewater treatment. The objectives of this study were to determine the effectiveness of nanoscale zerovalent iron (NZVI) to decolorize Reactive Black 5 (RB5) and Reactive Red 198 (RR198) in synthesized wastewater and to investigate the effects of the iron particle size, iron dosage and solution pHs on the destruction of RB5 and RR198. Synthesized NZVI was confirmed by transmission electron microscopy (TEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The removal kinetic rates (kobs) of RB5 (0.0109 min-1) and RR198 (0.0111 min-1) by 0.5% NZVI were many times higher than those of microscale zerovalent iron (ZVI) (0.0007 min-1 and 0.0008 min-1, respectively). The iron dosage increment exponentially increased the removal efficiencies of both RB5 and RR198. Additionally, lowering pH from 9 to 5 increased the decolorization kinetic rates of both RB5 and RR198 by NZVI. The destruction of azo bond (N=N) in the chromophore of both reactive dyes led to decolorization of dye solutions.
Abstract: A stiffened laminated composite panel (1 m length ×
0.5m width) was optimized for minimum weight and deflection under
several constraints using genetic algorithm. Here, a significant study
on the performance of a penalty function with two kinds of static and
dynamic penalty factors was conducted. The results have shown that
linear dynamic penalty factors are more effective than the static ones.
Also, a specially combined linear-exponential function has shown to
perform more effective than the previously mentioned penalty
functions. This was then resulted in the less sensitivity of the GA to
the amount of penalty factor.
Abstract: Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received significantly less attention. Instead, a model is usually described in terms of a stochastic process or a "higher-level paradigm" with graphical representation such as e.g. a stochastic Petri net. A serious problem then arises due to the exponential growth of the model-s state space which is in fact a main reason for the popularity of stochastic simulation since simulation suffers less from the state space explosion than non-simulative numerical solution techniques. In this paper we present transition class models for the representation of biological network models, a compact mathematical formalism that circumvents state space explosion. Transition class models can also serve as an interface between different higher level modeling paradigms, stochastic processes and the implementation coded in a programming language. Besides, the compact model representation provides the opportunity to apply non-simulative solution techniques thereby preserving the possible use of stochastic simulation. Illustrative examples of transition class representations are given for an enzyme-catalyzed substrate conversion and a part of the bacteriophage λ lysis/lysogeny pathway.
Abstract: This paper introduces a technique for simulating a
single-server exponential queuing system. The technique called the
Q-Simulator is a computer program which can simulate the effect of
traffic intensity on all system average quantities given the arrival
and/or service rates. The Q-Simulator has three phases namely: the
formula based method, the uncontrolled simulation, and the
controlled simulation. The Q-Simulator generates graphs (crystal
solutions) for all results of the simulation or calculation and can be
used to estimate desirable average quantities such as waiting times,
queue lengths, etc.
Abstract: These paper, we approximate the average run length
(ARL) for CUSUM chart when observation are an exponential first
order moving average sequence (EMA1). We used Gauss-Legendre
numerical scheme for integral equations (IE) method for approximate
ARL0 and ARL1, where ARL in control and out of control,
respectively. We compared the results from IE method and exact
solution such that the two methods perform good agreement.
Abstract: This work proposes an accurate crosstalk noise estimation method in the presence of multiple RLC lines for the use in design automation tools. This method correctly models the loading effects of non switching aggressors and aggressor tree branches using resistive shielding effect and realistic exponential input waveforms. Noise peak and width expressions have been derived. The results obtained are at good agreement with SPICE results. Results show that average error for noise peak is 4.7% and for the width is 6.15% while allowing a very fast analysis.
Abstract: In this paper, we consider the global exponential stability of the equilibrium point of Hopfield neural networks with delays and impulsive perturbation. Some new exponential stability criteria of the system are derived by using the Lyapunov functional method and the linear matrix inequality approach for estimating the upper bound of the derivative of Lyapunov functional. Finally, we illustrate two numerical examples showing the effectiveness of our theoretical results.
Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: The paper considers a single-server queue with fixedsize
batch Poisson arrivals and exponential service times, a model
that is useful for a buffer that accepts messages arriving as fixed size
batches of packets and releases them one packet at time. Transient
performance measures for queues have long been recognized as
being complementary to the steady-state analysis. The focus of the
paper is on the use of the functions that arise in the analysis of the
transient behaviour of the queuing system. The paper exploits
practical modelling to obtain a solution to the integral equation
encountered in the analysis. Results obtained indicate that under
heavy load conditions, there is significant disparity in the statistics
between the transient and steady state values.
Abstract: The aim of this paper is to present a methodology in
three steps to forecast supply chain demand. In first step, various data
mining techniques are applied in order to prepare data for entering
into forecasting models. In second step, the modeling step, an
artificial neural network and support vector machine is presented
after defining Mean Absolute Percentage Error index for measuring
error. The structure of artificial neural network is selected based on
previous researchers' results and in this article the accuracy of
network is increased by using sensitivity analysis. The best forecast
for classical forecasting methods (Moving Average, Exponential
Smoothing, and Exponential Smoothing with Trend) is resulted based
on prepared data and this forecast is compared with result of support
vector machine and proposed artificial neural network. The results
show that artificial neural network can forecast more precisely in
comparison with other methods. Finally, forecasting methods'
stability is analyzed by using raw data and even the effectiveness of
clustering analysis is measured.
Abstract: With the development of the Internet, E-commerce is
growing at an exponential rate, and lots of online stores are built up to
sell their goods online. A major factor influencing the successful
adoption of E-commerce is consumer-s trust. For new or unknown
Internet business, consumers- lack of trust has been cited as a major
barrier to its proliferation. As web sites provide key interface for
consumer use of E-Commerce, we investigate the design of web site to
build trust in E-Commerce from a design science approach. A
conceptual model is proposed in this paper to describe the ontology of
online transaction and human-computer interaction. Based on this
conceptual model, we provide a personalized webpage design
approach using Bayesian networks learning method. Experimental
evaluation are designed to show the effectiveness of web
personalization in improving consumer-s trust in new or unknown
online store.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.
Abstract: By using the method of coincidence degree theory and constructing suitable Lyapunov functional, several sufficient conditions are established for the existence and global exponential stability of anti-periodic solutions for Cohen-Grossberg shunting inhibitory neural networks with delays. An example is given to illustrate our feasible results.
Abstract: The objective of this research is to investigate the
advantages of using large-diameter 0.7 inch prestressing strands in
pretention applications. The advantages of large-diameter strands are
mainly beneficial in the heavy construction applications. Bridges and
tunnels are subjected to a higher daily traffic with an exponential
increase in trucks ultimate weight, which raise the demand for higher
structural capacity of bridges and tunnels. In this research, precast
prestressed I-girders were considered as a case study. Flexure
capacities of girders fabricated using 0.7 inch strands and different
concrete strengths were calculated and compared to capacities of 0.6
inch strands girders fabricated using equivalent concrete strength.
The effect of bridge deck concrete strength on composite deck-girder
section capacity was investigated due to its possible effect on final
section capacity. Finally, a comparison was made to compare the
bridge cross-section of girders designed using regular 0.6 inch strands
and the large-diameter 0.7 inch. The research findings showed that
structural advantages of 0.7 inch strands allow for using fewer bridge
girders, reduced material quantity, and light-weight members. The
structural advantages of 0.7 inch strands are maximized when high
strength concrete (HSC) are used in girder fabrication, and concrete
of minimum 5ksi compressive strength is used in pouring bridge
decks. The use of 0.7 inch strands in bridge industry can partially
contribute to the improvement of bridge conditions, minimize
construction cost, and reduce the construction duration of the project.
Abstract: There are many real world problems in which
parameters like the arrival time of new jobs, failure of resources, and
completion time of jobs change continuously. This paper tackles the
problem of scheduling jobs with random due dates on multiple
identical machines in a stochastic environment. First to assign jobs to
different machine centers LPT scheduling methods have been used,
after that the particular sequence of jobs to be processed on the
machine have been found using simple stochastic techniques. The
performance parameter under consideration has been the maximum
lateness concerning the stochastic due dates which are independent
and exponentially distributed. At the end a relevant problem has been
solved using the techniques in the paper..
Abstract: In this paper, we explore the applicability of the Sinc-
Collocation method to a three-dimensional (3D) oceanography model.
The model describes a wind-driven current with depth-dependent
eddy viscosity in the complex-velocity system. In general, the
Sinc-based methods excel over other traditional numerical methods
due to their exponentially decaying errors, rapid convergence and
handling problems in the presence of singularities in end-points.
Together with these advantages, the Sinc-Collocation approach that
we utilize exploits first derivative interpolation, whose integration
is much less sensitive to numerical errors. We bring up several
model problems to prove the accuracy, stability, and computational
efficiency of the method. The approximate solutions determined by
the Sinc-Collocation technique are compared to exact solutions and
those obtained by the Sinc-Galerkin approach in earlier studies. Our
findings indicate that the Sinc-Collocation method outperforms other
Sinc-based methods in past studies.
Abstract: In this paper, the robust exponential stability problem of discrete-time uncertain stochastic neural networks with timevarying delays is investigated. By introducing a new augmented Lyapunov function, some delay-dependent stable results are obtained in terms of linear matrix inequality (LMI) technique. Compared with some existing results in the literature, the conservatism of the new criteria is reduced notably. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.
Abstract: The paper is concerned with developing stochastic delay mechanisms for efficient multicast protocols and for smooth mobile handover processes which are capable of preserving a given Quality of Service (QoS). In both applications the participating entities (receiver nodes or subscribers) sample a stochastic timer and generate load after a random delay. In this way, the load on the networking resources is evenly distributed which helps to maintain QoS communication. The optimal timer distributions have been sought in different p.d.f. families (e.g. exponential, power law and radial basis function) and the optimal parameter have been found in a recursive manner. Detailed simulations have demonstrated the improvement in performance both in the case of multicast and mobile handover applications.
Abstract: In this paper, we have combined some spatial derivatives with the optimised time derivative proposed by Tam and Webb in order to approximate the linear advection equation which is given by = 0. Ôêé Ôêé + Ôêé Ôêé x f t u These spatial derivatives are as follows: a standard 7-point 6 th -order central difference scheme (ST7), a standard 9-point 8 th -order central difference scheme (ST9) and optimised schemes designed by Tam and Webb, Lockard et al., Zingg et al., Zhuang and Chen, Bogey and Bailly. Thus, these seven different spatial derivatives have been coupled with the optimised time derivative to obtain seven different finite-difference schemes to approximate the linear advection equation. We have analysed the variation of the modified wavenumber and group velocity, both with respect to the exact wavenumber for each spatial derivative. The problems considered are the 1-D propagation of a Boxcar function, propagation of an initial disturbance consisting of a sine and Gaussian function and the propagation of a Gaussian profile. It is known that the choice of the cfl number affects the quality of results in terms of dissipation and dispersion characteristics. Based on the numerical experiments solved and numerical methods used to approximate the linear advection equation, it is observed in this work, that the quality of results is dependent on the choice of the cfl number, even for optimised numerical methods. The errors from the numerical results have been quantified into dispersion and dissipation using a technique devised by Takacs. Also, the quantity, Exponential Error for Low Dispersion and Low Dissipation, eeldld has been computed from the numerical results. Moreover, based on this work, it has been found that when the quantity, eeldld can be used as a measure of the total error. In particular, the total error is a minimum when the eeldld is a minimum.
Abstract: In this paper, some new nonlinear generalized
Gronwall-Bellman-Type integral inequalities with mixed time delays
are established. These inequalities can be used as handy tools
to research stability problems of delayed differential and integral
dynamic systems. As applications, based on these new established
inequalities, some p-stable results of a integro-differential equation
are also given. Two numerical examples are presented to illustrate
the validity of the main results.