Search results for: forecast accuracy unemployment rate.

Authors: Mária Stachová, Lukáš Sobíšek

Abstract:

Data mining incorporates a group of statistical methods used to analyze a set of information, or a data set. It operates with models and algorithms, which are powerful tools with the great potential. They can help people to understand the patterns in certain chunk of information so it is obvious that the data mining tools have a wide area of applications. For example in the theoretical chemistry data mining tools can be used to predict moleculeproperties or improve computer-assisted drug design. Classification analysis is one of the major data mining methodologies. The aim of thecontribution is to create a classification model, which would be able to deal with a huge data set with high accuracy. For this purpose logistic regression, Bayesian logistic regression and random forest models were built using R software. TheBayesian logistic regression in Latent GOLD software was created as well. These classification methods belong to supervised learning methods. It was necessary to reduce data matrix dimension before construct models and thus the factor analysis (FA) was used. Those models were applied to predict the biological activity of molecules, potential new drug candidates.

Keywords: data mining, classification, drug design, QSAR

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

Abstract:

Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: Long-short-term memory, re-identification, security critical application, spatial-temporal awareness.

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Authors: Roland Pusch, Richard Weston

Abstract:

The scale dependence of the strength of virtually homogeneous rock is usually considered to be insignificant but the spectrum of discontinuities plays a very important role for the strength of differently sized rock elements and also controls the rock creep strain. Large-scale load tests comprised recording of the creep strain rate that was found to be strongly retarded and negligible for stresses lower than about 1/3 of the failure load. For higher stresses creep took place according to a log time law representing secondary creep that ultimately changed to tertiary creep and failure.

Keywords: Impact of scale, rock strength.

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Authors: Yung-Yuan Hsu

Abstract:

The primary objective of this paper was to construct a “kinematic parameter-independent modeling of three-axis machine tools for geometric error measurement" technique. Improving the accuracy of the geometric error for three-axis machine tools is one of the machine tools- core techniques. This paper first applied the traditional method of HTM to deduce the geometric error model for three-axis machine tools. This geometric error model was related to the three-axis kinematic parameters where the overall errors was relative to the machine reference coordinate system. Given that the measurement of the linear axis in this model should be on the ideal motion axis, there were practical difficulties. Through a measurement method consolidating translational errors and rotational errors in the geometric error model, we simplified the three-axis geometric error model to a kinematic parameter-independent model. Finally, based on the new measurement method corresponding to this error model, we established a truly practical and more accurate error measuring technique for three-axis machine tools.

Keywords: Three-axis machine tool, Geometric error, HTM, Error measuring

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Authors: Habibis Saleh, Ishak Hashim

Abstract:

Conjugate natural convection in a differentially heated square enclosure containing a polygon shaped object is studied numerically in this article. The effect of various polygon types on the fluid flow and thermal performance of the enclosure is addressed for different thermal conductivities. The governing equations are modeled and solved numerically using the built-in finite element method of COMSOL software. It is found that the heat transfer rate remains stable by varying the polygon types.

Keywords: Natural convection, Polygon object, COMSOL

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Authors: Muhammad F. Yaqub, Iqbal Gondal, Joarder Kamruzzaman

Abstract:

Occurrence of a multiple-points fault in machine operations could result in exhibiting complex fault signatures, which could result in lowering fault diagnosis accuracy. In this study, a multiple-points defect model (MPDM) is proposed which can simulate fault signature-s dynamics for n-points bearing faults. Furthermore, this study identifies that in case of multiple-points fault in the rotary machine, the location of the dominant component of defect frequency shifts depending upon the relative location of the fault points which could mislead the fault diagnostic model to inaccurate detections. Analytical and experimental results are presented to characterize and validate the variation in the dominant component of defect frequency. Based on envelop detection analysis, a modification is recommended in the existing fault diagnostic models to consider the multiples of defect frequency rather than only considering the frequency spectrum at the defect frequency in order to incorporate the impact of multiple points fault.

Keywords: Envelop detection, machine defect frequency, multiple faults, machine health monitoring.

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Authors: Priyanka Malik A, Rishu Chaujar B, Mridula Gupta C, R.S. Gupta D

Abstract:

In this paper, for the first time, a two-dimensional (2D) analytical drain current model for sub-100 nm multi-layered gate material engineered trapezoidal recessed channel (MLGMETRC) MOSFET: a novel design is presented and investigated using ATLAS and DEVEDIT device simulators, to mitigate the large gate leakages and increased standby power consumption that arise due to continued scaling of SiO2-based gate dielectrics. The twodimensional (2D) analytical model based on solution of Poisson-s equation in cylindrical coordinates, utilizing the cylindrical approximation, has been developed which evaluate the surface potential, electric field, drain current, switching metric: ION/IOFF ratio and transconductance for the proposed design. A good agreement between the model predictions and device simulation results is obtained, verifying the accuracy of the proposed analytical model.

Keywords: ATLAS, DEVEDIT, NJD, MLGME- TRCMOSFET.

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

Abstract:

Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: Bioassay, machine learning, preprocessing, virtual screen.

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Authors: Chisato Murakami, Makoto Takahashi

Abstract:

Force sensor has been used as requisite for knowing information on the amount and the directions of forces on the skin surface. We have developed a four-degrees-of-freedom capacitive force sensor (approximately 20×20×5 mm³) that has a flexible structure and sixteen parallel plate capacitors. An iterative algorithm was developed for estimating four displacements from the sixteen capacitances using fourth-order polynomial approximation of characteristics between capacitance and displacement. The estimation results from measured capacitances had large error caused by deterioration of the characteristics. In this study, effective capacitors had major information were selected on the basis of the capacitance change range and the characteristic shape. Maximum errors in calibration and non-calibration points were 25%and 6.8%.However the maximum error was larger than desired value, the smallness of averaged value indicated the occurrence of a few large error points. On the other hand, error in non-calibration point was within desired value.

 

Keywords: Force sensors, capacitive sensors, estimation, iterative algorithms.

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Authors: ChangWoo Lee, Young Kow Lee, Sung Jun Ban, SungHoo Choi, Sang Woo Kim

Abstract:

This paper suggests a new Affine Projection (AP) algorithm with variable data-reuse factor using the condition number as a decision factor. To reduce computational burden, we adopt a recently reported technique which estimates the condition number of an input data matrix. Several simulations show that the new algorithm has better performance than that of the conventional AP algorithm.

Keywords: Affine projection algorithm, variable data-reuse factor, condition number, convergence rate, misalignment.

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Authors: Muhibul Haque Bhuyan

Abstract:

Diabetes disease that arises from the higher glucose level due to insulin shortage or insulin opposition in the human body has become a common disease in the world. No medicine can cure it completely. However, by taking medicine, maintaining diets, and having exercises regularly, a diabetes patient can keep his glucose level within the specified limits and in this way, he/she can lead a normal life like a healthy person. But to control glucose levels, a patient needs to monitor them regularly. Various techniques are being used over the last four decades. This modern review article aims to provide a comparative study report on various blood glucose monitoring techniques in a very concise and organized manner. The review mainly emphasizes working principles, cost, technology, sensors, measurement types, measurement accuracy, advantages, and disadvantages, etc. of various techniques and then compares among each other. Besides, the use of algorithms and simulators for the growth of this technology is also presented. Finally, current research trends of this measurement technology have also been discussed.

Keywords: blood glucose measurement, sensors, measurement devices, invasive and non-invasive techniques

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Authors: Sozon H. Papavlasopoulos, Marios S. Poulos, George D. Bokos, Angelos M. Evangelou

Abstract:

In this study, the problem of discriminating between interictal epileptic and non- epileptic pathological EEG cases, which present episodic loss of consciousness, investigated. We verify the accuracy of the feature extraction method of autocross-correlated coefficients which extracted and studied in previous study. For this purpose we used in one hand a suitable constructed artificial supervised LVQ1 neural network and in other a cross-correlation technique. To enforce the above verification we used a statistical procedure which based on a chi- square control. The classification and the statistical results showed that the proposed feature extraction is a significant accurate method for diagnostic discrimination cases between interictal and non-interictal EEG events and specifically the classification procedure showed that the LVQ neural method is superior than the cross-correlation one.

Keywords: Cross-Correlation Methods, Diagnostic Test, Interictal Epileptic, LVQ1 neural network, Auto-Cross-Correlation Methods, chi-square test.

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Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: D. I. De Souza, D. R. Fonseca, D. Kipper

Abstract:

In this paper, the test purpose will be to assess whether or not the accelerated model proposed by Eyring will be able to translate results for the shape and scale parameters of an underlying Weibull model, obtained under two accelerating using conditions, to expected normal using condition results for these parameters. The product being analyzed is a new type of insulate fluid, and the accelerating factor is the voltage stresses applied to the fluid at two different levels (30KV and 40KV). The normal operating voltage is 25KV. In this case, it was possible to test the insulate fluid at normal voltage using condition. Both results for the two parameters of the Weibull model, obtained under normal using condition and translated from accelerated using conditions to normal conditions, will be compared to each other to assess the accuracy of the Eyring model when the accelerating factor is only the voltage stress.

Keywords: Eyring Accelerated Model, Sequential Life Testing, Two-Parameter Weibull Distribution, Voltage Stresses.

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Authors: Vitaliy Tayanov

Abstract:

In this paper we present the algorithm which allows us to have an object tracking close to real time in Full HD videos. The frame rate (FR) of a video stream is considered to be between 5 and 30 frames per second. The real time track building will be achieved if the algorithm can follow 5 or more frames per second. The principle idea is to use fast algorithms when doing preprocessing to obtain the key points and track them after. The procedure of matching points during assignment is hardly dependent on the number of points. Because of this we have to limit pointed number of points using the most informative of them.

Keywords: video tracking, real-time, Hungarian algorithm, Full HD video.

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Authors: H. Soleimanjahi, M. J. Nategh, S. Falahi

Abstract:

Deciding the numerous parameters involved in designing a competent artificial neural network is a complicated task. The existence of several options for selecting an appropriate architecture for neural network adds to this complexity, especially when different applications of heterogeneous natures are concerned. Two completely different applications in engineering and medical science were selected in the present study including prediction of workpiece's surface roughness in ultrasonic-vibration assisted turning and papilloma viruses oncogenicity. Several neural network architectures with different parameters were developed for each application and the results were compared. It was illustrated in this paper that some applications such as the first one mentioned above are apt to be modeled by a single network with sufficient accuracy, whereas others such as the second application can be best modeled by different expert networks for different ranges of output. Development of knowledge about the essentials of neural networks for different applications is regarded as the cornerstone of multidisciplinary network design programs to be developed as a means of reducing inconsistencies and the burden of the user intervention.

Keywords: Artificial Neural Network, Malignancy Diagnosis, Papilloma Viruses Oncogenicity, Surface Roughness, UltrasonicVibration-Assisted Turning.

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Authors: Khairul Nizam Tahar, Anuar Ahmad

Abstract:

This paper has introduced a slope photogrammetric mapping using unmanned aerial vehicle. There are two units of UAV has been used in this study; namely; fixed wing and multi-rotor. Both UAVs were used to capture images at the study area. A consumer digital camera was mounted vertically at the bottom of UAV and captured the images at an altitude. The objectives of this study are to obtain three dimensional coordinates of slope area and to determine the accuracy of photogrammetric product produced from both UAVs. Several control points and checkpoints were established Real Time Kinematic Global Positioning System (RTK-GPS) in the study area. All acquired images from both UAVs went through all photogrammetric processes such as interior orientation, exterior orientation, aerial triangulation and bundle adjustment using photogrammetric software. Two primary results were produced in this study; namely; digital elevation model and digital orthophoto. Based on results, UAV system can be used to mapping slope area especially for limited budget and time constraints project.

Keywords: Slope mapping, 3D, DEM, UAV, Photogrammetry, image processing.

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Authors: Tamara Anderson, Neela Badrie

Abstract:

Consumers are demanding novel beverages that are healthier, convenient and have appealing consumer acceptance. The objectives of this study were to investigate the effects of adding grape polyphenols and the influence of presenting health claims on the sensory acceptability of wines. Fresh red sorrel calyces were fermented into wines. The total soluble solids of the pectinase-treated sorrel puree were from 4°Brix to 23.8°Brix. Polyphenol in the form of grape pomace extract was added to sorrel wines (w/v) in specified levels to give 0. 25. 50 and 75 ppm. A focus group comprising of 12 panelists was use to select the level of polyphenol to be added to sorrel wines for sensory preference The sensory attributed of the wines which were evaluated were colour, clarity, aroma, flavor, mouth-feel, sweetness, astringency and overall preference. The sorrel wine which was most preferred from focus group evaluation was presented for hedonic rating. In the first stage of hedonic testing, the sorrel wine was served chilled at 7°C for 24 h prior to sensory evaluation. Each panelist was provided with a questionnaire and was asked to rate the wines on colour, aroma, flavor, mouth-feel, sweetness, astringency and overall acceptability using a 9-point hedonic scale. In the second stage of hedonic testing, the panelist were instructed to read a health abstract on the health benefits of polyphenolic compounds and again to rate sorrel wine with added 25 ppm polyphenol. Paired t-test was used for the analysis of the influence of presenting health information on polyphenols on hedonic scoring of sorrel wines. Focus groups found that the addition of polyphenol addition had no significant effect on sensory color and aroma but affected clarity and flavor. A 25 ppm wine was liked moderately in overall acceptability. The presentation of information on the health benefit of polyphenols in sorrel wines to panelists had no significant influence on the sensory acceptance of wine. More than half of panelists would drink this wine now and then. This wine had color L 19.86±0.68, chroma 2.10±0.12, hue° 16.90 ±3.10 and alcohol content of 13.0%. The sorrel wine was liked moderately in overall acceptability with the added polyphenols.

Keywords: Sorrel wines, Roselle Hibiscus sabdariffa L, novel wine, polyphenols, health benefits, physicochemical properties.

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Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

Abstract:

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: Localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI.

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Authors: S. Makal, L. Ozyilmaz, S. Palavaroglu

Abstract:

Gene, principal unit of inheritance, is an ordered sequence of nucleotides. The genes of eukaryotic organisms include alternating segments of exons and introns. The region of Deoxyribonucleic acid (DNA) within a gene containing instructions for coding a protein is called exon. On the other hand, non-coding regions called introns are another part of DNA that regulates gene expression by removing from the messenger Ribonucleic acid (RNA) in a splicing process. This paper proposes to determine splice junctions that are exon-intron boundaries by analyzing DNA sequences. A splice junction can be either exon-intron (EI) or intron exon (IE). Because of the popularity and compatibility of the artificial neural network (ANN) in genetic fields; various ANN models are applied in this research. Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression Neural Networks (GRNN) are used to analyze and detect the splice junctions of gene sequences. 10-fold cross validation is used to demonstrate the accuracy of networks. The real performances of these networks are found by applying Receiver Operating Characteristic (ROC) analysis.

Keywords: Gene, neural networks, ROC analysis, splice junctions.

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Authors: K. Nirmala Devi, V. Murali Bhaskaran

Abstract:

The exponential growth of social media arouses much attention on public opinion information. The online forums, blogs, micro blogs are proving to be extremely valuable resources and are having bulk volume of information. However, most of the social media data is unstructured and semi structured form. So that it is more difficult to decipher automatically. Therefore, it is very much essential to understand and analyze those data for making a right decision. The online forums hotspot detection is a promising research field in the web mining and it guides to motivate the user to take right decision in right time. The proposed system consist of a novel approach to detect a hotspot forum for any given time period. It uses aging theory to find the hot terms and E-K-means for detecting the hotspot forum. Experimental results demonstrate that the proposed approach outperforms k-means for detecting the hotspot forums with the improved accuracy.

Keywords: Hotspot forums, Micro blog, Blog, Sentiment Analysis, Opinion Mining, Social media, Twitter, Web mining.

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Authors: A. Brahma

Abstract:

Drying is a phenomenon that accompanies the hardening of hydraulic materials. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes consideration of the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: Drying, hydraulic concretes, shrinkage, modeling, prediction.

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Authors: Mehdi Shamshiri, Mahmud Ashrafizaadeh

Abstract:

The significant effects of the interactions between the system boundaries and the near wall molecules in miniaturized gaseous devices lead to the formation of the Knudsen layer in which the Navier-Stokes-Fourier (NSF) equations fail to predict the correct associated phenomena. In this paper, the well-known lattice Boltzmann method (LBM) is employed to simulate the fluid flow and heat transfer processes in rarefied gaseous micro media. Persuaded by the problematic deficiency of the LBM in capturing the Knudsen layer phenomena, present study tends to concentrate on the effective molecular mean free path concept the main essence of which is to compensate the incapability of this mesoscopic method in dealing with the momentum and energy transport within the above mentioned kinetic boundary layer. The results show qualitative and quantitative accuracy comparable to the solutions of the linearized Boltzmann equation or the DSMC data for the Knudsen numbers of O (1) .

Keywords: Fluid flow and Heat transfer, Knudsen layer, Lattice Boltzmann method (LBM), Micro-scale numerical simulation, Transition regime.

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Authors: V. Vicente E. Mujica, Gustavo Gonzalez

Abstract:

The global coverage of broadband multimedia and internet-based services in terrestrial-satellite networks demand particular interests for satellite providers in order to enhance services with low latencies and high signal quality to diverse users. In particular, the delay of on-board processing is an inherent source of latency in a satellite communication that sometimes is discarded for the end-to-end delay of the satellite link. The frame work for this paper includes modelling of an on-orbit satellite payload using an agent model that can reproduce the properties of processing delays. In essence, a comparison of different spatial interpolation methods is carried out to evaluate physical data obtained by an GEO satellite in order to define a discretization function for determining that delay. Furthermore, the performance of the proposed agent and the development of a delay discretization function are together validated by simulating an hybrid satellite and terrestrial network. Simulation results show high accuracy according to the characteristics of initial data points of processing delay for Ku bands.

Keywords: Terrestrial-satellite networks, latency, on-orbit satellite payload, simulation.

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Authors: Khosro Mohammadi

Abstract:

The grain quality of chickpea in Iran is low and instable, which may be attributed to the evolution of cultivars with a narrow genetic base making them vulnerable to biotic stresses. Four chickpea varieties from diverse geographic origins were chosen and arranged in a randomized complete block design. Mesorhizobium sp. cicer strain SW7 was added to all the chickpea seeds. Chickpea seeds were planted on October 9, 2013. Each genotype was sown 5 m in length, with 35 cm inter-row spacing, in 3 rows. Weeds were removed manually in all plots. Results showed that Analysis of variance on the studied traits showed significant differences among genotypes for N, P, K and Fe contents of chickpea, but there is not a significant difference among Ca, Zn and Mg continents of chickpea. The experimental coefficient of variation (CV) varied from 7.3 to 15.8. In general, the CV value lower than 20% is considered to be good, indicating the accuracy of conducted experiments. The highest grain N was observed in Hashem and Jam cultivars. The highest grain P was observed in Jam cultivar. Phosphorus content (mg/100g) ranged from 142.3 to 302.3 with a mean value of 221.3. The negative correlation (-0.126) was observed between the N and P of chickpea cultivars. The highest K and Fe contents were observed in Jam cultivar.

Keywords: Cultivar, genotype, nitrogen, nutrient, yield.

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Authors: Ashraf A. Eltholth, Adel R. Mikhail, A. Elshirbini, Moawad I. Moawad, A. I. Abdelfattah

Abstract:

In this paper we proposed the use of Huffman coding to reduce the PAR of an OFDM system as a distortionless scrambling technique, and we utilize the amount saved in the total bit rate by the Huffman coding to send the encoding table for accurate decoding at the receiver without reducing the effective throughput. We found that the use of Huffman coding reduces the PAR by about 6 dB. Also we have investigated the effect of PAR reduction due to Huffman coding through testing the spectral spreading and the inband distortion due to HPA with different IBO values. We found a complete match of our expectation from the proposed solution with the obtained simulation results.

Keywords: HPA, Huffman coding, OFDM, PAR

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Authors: S. Raissi

Abstract:

Response surface methodology (RSM) is a very efficient tool to provide a good practical insight into developing new process and optimizing them. This methodology could help engineers to raise a mathematical model to represent the behavior of system as a convincing function of process parameters. Through this paper the sequential nature of the RSM surveyed for process engineers and its relationship to design of experiments (DOE), regression analysis and robust design reviewed. The proposed four-step procedure in two different phases could help system analyst to resolve the parameter design problem involving responses. In order to check accuracy of the designed model, residual analysis and prediction error sum of squares (PRESS) described. It is believed that the proposed procedure in this study can resolve a complex parameter design problem with one or more responses. It can be applied to those areas where there are large data sets and a number of responses are to be optimized simultaneously. In addition, the proposed procedure is relatively simple and can be implemented easily by using ready-made standard statistical packages.

Keywords: Response Surface Methodology (RSM), Design of Experiments (DOE), Process modeling, Process setting, Process optimization.

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Authors: A. Golmohammadi, B. Jahandideh

Abstract:

One of the determinants of a firm-s prosperity is the customers- perceived service quality and satisfaction. While service quality is wide in scope, and consists of various dimensions, there may be differences in the relative importance of these dimensions in affecting customers- overall satisfaction of service quality. Identifying the relative rank of different dimensions of service quality is very important in that it can help managers to find out which service dimensions have a greater effect on customers- overall satisfaction. Such an insight will consequently lead to more effective resource allocation which will finally end in higher levels of customer satisfaction. This issue – despite its criticality- has not received enough attention so far. Therefore, using a sample of 240 bank customers in Iran, an artificial neural network is developed to address this gap in the literature. As customers- evaluation of service quality is a subjective process, artificial neural networks –as a brain metaphor- may appear to have a potentiality to model such a complicated process. Proposing a neural network which is able to predict the customers- overall satisfaction of service quality with a promising level of accuracy is the first contribution of this study. In addition, prioritizing the service quality dimensions in affecting customers- overall satisfaction –by using sensitivity analysis of neural network- is the second important finding of this paper.

Keywords: service quality, customer satisfaction, relative importance, artificial neural network.

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Authors: Vineet K. Srivastava, Mukesh K. Awasthi, Mohammad Tamsir

Abstract:

A fully implicit finite-difference method has been proposed for the numerical solutions of one dimensional coupled nonlinear Burgers’ equations on the uniform mesh points. The method forms a system of nonlinear difference equations which is to be solved at each iteration. Newton’s iterative method has been implemented to solve this nonlinear assembled system of equations. The linear system has been solved by Gauss elimination method with partial pivoting algorithm at each iteration of Newton’s method. Three test examples have been carried out to illustrate the accuracy of the method. Computed solutions obtained by proposed scheme have been compared with analytical solutions and those already available in the literature by finding L2 and L∞ errors.

Keywords: Burgers’ equation, Implicit Finite-difference method, Newton’s method, Gauss elimination with partial pivoting.

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Authors: Mehmet Savsar

Abstract:

This paper presents a model for an unreliable production line, which is operated according to demand with constant work-in-process (CONWIP). A simulation model is developed based on the discrete model and several case problems are analyzed using the model. The model is utilized to optimize storage space capacities at intermediate stages and the number of kanbans at the last stage, which is used to trigger the production at the first stage. Furthermore, effects of several line parameters on production rate are analyzed using design of experiments.

Keywords: Production line simulator, Push-pull system, JIT system, Constant WIP, Machine failures.

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