An Assessment of Water Pollution of the Beshar River Aquatic Ecosystems

The Beshar River is one of the most important aquatic ecosystems in the upstream of the Karun watershed in south of Iran which is affected by point and non point pollutant sources . This study was done in order to evaluate the effects of pollutants activities on the water quality of the Beshar river and its aquatic ecosystems. This river is approximately 190 km in length and situated at the geographical positions of 51° 20´ to 51° 48´ E and 30° 18´ to 30° 52´ N it is one of the most important aquatic ecosystems of Kohkiloye and Boyerahmad province in south-west Iran. In this research project, five study stations were selected to examine water pollution in the Beshar River systems. Human activity is now one of the most important factors affecting on hydrology and water quality of the Beshar river. Humans use large amounts of resources to sustain various standards of living, although measures of sustainability are highly variable depending on how sustainability is defined. The Beshar river ecosystems are particularly sensitive and vulnerable to human activities. Therefore, to determine the impact of human activities on the Beshar River, the most important water quality parameters such as pH, dissolve oxygen (DO), Biological Oxygen Demand (BOD5), Total Dissolve Solids (TDS), Nitrates (NO3-N) and Phosphates (PO4) were estimated at the five stations. As the results show, the most important pollution index parameters such as BOD5, NO3 and PO4 increase and DO and pH decrease according to human activities (P

A Fuzzy Predictive Filter for Sinusoidal Signals with Time-Varying Frequencies

Prediction of sinusoidal signals with time-varying frequencies has been an important research topic in power electronics systems. To solve this problem, we propose a new fuzzy predictive filtering scheme, which is based on a Finite Impulse Response (FIR) filter bank. Fuzzy logic is introduced here to provide appropriate interpolation of individual filter outputs. Therefore, instead of regular 'hard' switching, our method has the advantageous 'soft' switching among different filters. Simulation comparisons between the fuzzy predictive filtering and conventional filter bank-based approach are made to demonstrate that the new scheme can achieve an enhanced prediction performance for slowly changing sinusoidal input signals.

Impact of the Decoder Connection Schemes on Iterative Decoding of GPCB Codes

In this paper we present a study of the impact of connection schemes on the performance of iterative decoding of Generalized Parallel Concatenated block (GPCB) constructed from one step majority logic decodable (OSMLD) codes and we propose a new connection scheme for decoding them. All iterative decoding connection schemes use a soft-input soft-output threshold decoding algorithm as a component decoder. Numerical result for GPCB codes transmitted over Additive White Gaussian Noise (AWGN) channel are provided. It will show that the proposed scheme is better than Hagenauer-s scheme and Lucas-s scheme [1] and slightly better than the Pyndiah-s scheme.

Method of Moments for Analysis of Multiple Crack Interaction in an Isotropic Elastic Solid

The problem of N cracks interaction in an isotropic elastic solid is decomposed into a subproblem of a homogeneous solid without crack and N subproblems with each having a single crack subjected to unknown tractions on the two crack faces. The unknown tractions, namely pseudo tractions on each crack are expanded into polynomials with unknown coefficients, which have to be determined by the consistency condition, i.e. by the equivalence of the original multiple cracks interaction problem and the superposition of the N+1 subproblems. In this paper, Kachanov-s approach of average tractions is extended into the method of moments to approximately impose the consistence condition. Hence Kachanov-s method can be viewed as the zero-order method of moments. Numerical results of the stress intensity factors are presented for interactions of two collinear cracks, three collinear cracks, two parallel cracks, and three parallel cracks. As the order of moment increases, the accuracy of the method of moments improves.

Optimizing Electrospinning Parameters for Finest Diameter of Nano Fibers

Nano fibers produced by electrospinning are of industrial and scientific attention due to their special characteristics such as long length, small diameter and high surface area. Applications of electrospun structures in nanotechnology are included tissue scaffolds, fibers for drug delivery, composite reinforcement, chemical sensing, enzyme immobilization, membrane-based filtration, protective clothing, catalysis, solar cells, electronic devices and others. Many polymer and ceramic precursor nano fibers have been successfully electrospun with diameters in the range from 1 nm to several microns. The process is complex so that fiber diameter is influenced by various material, design and operating parameters. The objective of this work is to apply genetic algorithm on the parameters of electrospinning which have the most significant effect on the nano fiber diameter to determine the optimum parameter values before doing experimental set up. Effective factors including initial polymer concentration, initial jet radius, electrical potential, relaxation time, initial elongation, viscosity and distance between nozzle and collector are considered to determine finest diameter which is selected by user.

Gas Turbine Optimal PID Tuning by Genetic Algorithm using MSE

Realistic systems generally are systems with various inputs and outputs also known as Multiple Input Multiple Output (MIMO). Such systems usually prove to be complex and difficult to model and control purposes. Therefore, decomposition was used to separate individual inputs and outputs. A PID is assigned to each individual pair to regulate desired settling time. Suitable parameters of PIDs obtained from Genetic Algorithm (GA), using Mean of Squared Error (MSE) objective function.

Enzymatic Activity of Alfalfa in a Phenanthrene-contaminated Environment

This research was undertaken to study enzymatic activity in the shoots, roots, and rhizosphere of alfalfa (Medicago sativa L.) grown in quartz sand that was uncontaminated and contaminated with phenanthrene at concentrations of 10 and 100 mg kg-1. The higher concentration of phehanthrene had a distinct phytotoxic effect on alfalfa, inhibiting seed germination energy, plant survival, and biomass accumulation. The plant stress response to the environmental pollution was an increase in peroxidase activity. Peroxidases were the predominant enzymes in the alfalfa shoots and roots. The peroxidase profile in the shoots differed from that in the roots and had different isoenzyme numbers. 2,2'-Azinobis-(3-ethylbenzo-thiazoline-6-sulphonate) (ABTS) peroxidase was predominant in the shoots, and 2,7-diaminofluorene (2,2-DAF) peroxidase was predominant in the roots. Under the influence of phenanthrene, the activity of 2,7-DAF peroxidase increased in the shoots, and the activity of ABTS peroxidase increased in the roots. Alfalfa root peroxidases were the prevalent enzyme systems in the rhizosphere sand. Examination of the activity of alfalfa root peroxidase toward phenanthrene revealed the possibility of involvement of the plant enzyme in rhizosphere degradation of the PAH.

Development of a New CFD Multi-Coupling Tool Based on Immersed Boundary Method: toward SRM Analysis

The ongoing effort to develop an in-house compressible solver with multi-disciplinary physics is presented in this paper. Basic compressible solver combined with IBM technique provides us an effective numerical tool able to tackle the physics phenomena and especially physic phenomena involved in Solid Rocket Motors (SRMs). Main principles are introduced step by step describing its implementation. This paper sheds light on the whole potentiality of our proposed numerical model and we strongly believe a way to introduce multi-physics mechanisms strongly coupled is opened to ablation in nozzle, fluid/structure interaction and burning propellant surface with time.

Novel SNC-NN-MRAS Based Speed Estimator for Sensor-Less Vector Controlled IM Drives

Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most popularly used conventional speed estimation scheme for sensor-less IM drives. In this scheme, the voltage model equations are used for the reference model. This encounters major drawbacks at low frequencies/speed which leads to the poor performance of RF-MRAS. Replacing the reference model using Neural Network (NN) based flux estimator provides an alternate solution and addresses such drawbacks. This paper identifies an NN based flux estimator using Single Neuron Cascaded (SNC) Architecture. The proposed SNC-NN model replaces the conventional voltage model in RF-MRAS to form a novel MRAS scheme named as SNC-NN-MRAS. Through simulation the proposed SNC-NN-MRAS is shown to be promising in terms of all major issues and robustness to parameter variation. The suitability of the proposed SNC-NN-MRAS based speed estimator and its advantages over RF-MRAS for sensor-less induction motor drives is comprehensively presented through extensive simulations.

Flagging Critical Components to Prevent Transient Faults in Real-Time Systems

This paper proposes the use of metrics in design space exploration that highlight where in the structure of the model and at what point in the behaviour, prevention is needed against transient faults. Previous approaches to tackle transient faults focused on recovery after detection. Almost no research has been directed towards preventive measures. But in real-time systems, hard deadlines are performance requirements that absolutely must be met and a missed deadline constitutes an erroneous action and a possible system failure. This paper proposes the use of metrics to assess the system design to flag where transient faults may have significant impact. These tools then allow the design to be changed to minimize that impact, and they also flag where particular design techniques – such as coding of communications or memories – need to be applied in later stages of design.

The System Identification and PID Lead-lag Control for Two Poles Unstable SOPDT Process by Improved Relay Method

This paper describes identification of the two poles unstable SOPDT process, especially with large time delay. A new modified relay feedback identification method for two poles unstable SOPDT process is proposed. Furthermore, for the two poles unstable SOPDT process, an additional Derivative controller is incorporated parallel with relay to relax the constraint on the ratio of delay to the unstable time constant, so that the exact model parameters of unstable processes can be identified. To cope with measurement noise in practice, a low pass filter is suggested to get denoised output signal toimprove the exactness of model parameter of unstable process. PID Lead-lag tuning formulas are derived for two poles unstable (SOPDT) processes based on IMC principle. Simulation example illustrates the effectiveness and the simplicity of the proposed identification and control method.

Relational Representation in XCSF

Generalization is one of the most challenging issues of Learning Classifier Systems. This feature depends on the representation method which the system used. Considering the proposed representation schemes for Learning Classifier System, it can be concluded that many of them are designed to describe the shape of the region which the environmental states belong and the other relations of the environmental state with that region was ignored. In this paper, we propose a new representation scheme which is designed to show various relationships between the environmental state and the region that is specified with a particular classifier.

A New Proportional - Pursuit Coupled Guidance Law with Actuator Delay Compensation

The aim of this paper is to present a new three-dimensional proportional-pursuit coupled (PP) guidance law to track highly maneuverable aircraft. Utilizing a 3-D polar coordinate frame, the PP guidance law is formed by collecting proportional navigation guidance in Z-R plane and pursuit guidance in X-Y plane. Feedback linearization control method to solve the guidance accelerations is used to implement PP guidance. In order to compensate the actuator time delay, the time delay compensated version of PP guidance law (CPP) was derived and proved the effectiveness of modifying the problem of high acceleration in the final phase of pursuit guidance and improving the weak robustness of proportional navigation. The simulation results for intercepting Max G turn situation show that the proposed proportional-pursuit coupled guidance law guidance law with actuator delay compensation (CPP) possesses satisfactory robustness and performance.

Confidence Intervals for the Difference of Two Normal Population Variances

Motivated by the recent work of Herbert, Hayen, Macaskill and Walter [Interval estimation for the difference of two independent variances. Communications in Statistics, Simulation and Computation, 40: 744-758, 2011.], we investigate, in this paper, new confidence intervals for the difference between two normal population variances based on the generalized confidence interval of Weerahandi [Generalized Confidence Intervals. Journal of the American Statistical Association, 88(423): 899-905, 1993.] and the closed form method of variance estimation of Zou, Huo and Taleban [Simple confidence intervals for lognormal means and their differences with environmental applications. Environmetrics 20: 172-180, 2009]. Monte Carlo simulation results indicate that our proposed confidence intervals give a better coverage probability than that of the existing confidence interval. Also two new confidence intervals perform similarly based on their coverage probabilities and their average length widths.

Restoration of Noisy Document Images with an Efficient Bi-Level Adaptive Thresholding

An effective approach for extracting document images from a noisy background is introduced. The entire scheme is divided into three sub- stechniques – the initial preprocessing operations for noise cluster tightening, introduction of a new thresholding method by maximizing the ratio of stan- dard deviations of the combined effect on the image to the sum of weighted classes and finally the image restoration phase by image binarization utiliz- ing the proposed optimum threshold level. The proposed method is found to be efficient compared to the existing schemes in terms of computational complexity as well as speed with better noise rejection.

Determining the Principles Affecting Perceptions of Strategic Quality Management Implementation: A Study of the Turkish Large Scale Firms

The purpose of this study is to reveal the principles, which have the highest impact on determining the Strategic Quality Management (SQM) implementation perceptions of managers. In order to accomplish this goal, first of all, a factor analysis is conducted on the attitudes of managers at 80 large-scale firms in Turkey for SQM principles. Secondly, utilizing t tests and discriminant analysis, the most effective items are determined. The results show that “process improvement" and “assessment of competitiveness" are the management principles, which have the highest impact on determining the SQM implementation perceptions of Turkish managers.

Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents

In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.

Changes in Behavior and Learning Ability of Rats Intoxicated with Lead

Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10) and control group did not receive lead acetate (n=5) from 8th day of pregnancy till weaning of pups. The obtained results revealed a dose dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control, on contrary lying time decreased gradually in a dose dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while, they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than control group.

Wavelet-Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters

In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.

Mathematical Modelling of Single Phase Unity Power Factor Boost Converter

An optimal control strategy based on simple model, a single phase unity power factor boost converter is presented with an evaluation of first order differential equations. This paper presents an evaluation of single phase boost converter having power factor correction. The simple discrete model of boost converter is formed and optimal control is obtained, digital PI is adopted to adjust control error. The method of instantaneous current control is proposed in this paper for its good tracking performance of dynamic response. The simulation and experimental results verified our design.