Search results for: Functions of random variables.

Authors: Bhaveshkumar P. Patel, J. M. Prajapati

Abstract:

Excavators are high power machines used in the mining, agricultural and construction industry whose principal functions are digging (material removing), ground leveling and material transport operations. During the digging task there are certain unknown forces exerted by the bucket on the soil and the digging operation is repetitive in nature. Automation of the digging task can be performed by an automatically controlled excavator system, which is not only control the forces but also follow the planned digging trajectories. To develop such a controller for automated excavation, it is required to develop a dynamic model to describe the behavior of the control system during digging operation and motion of excavator with time. The presented work described a dynamic model needed for controller design and which is derived by applying Lagrange-Euler approach. The developed dynamic model is intended for further development of an automated excavation control system for light duty construction work and can be applied for heavy duty or all types of backhoe excavators.

Keywords: Backhoe excavator, controller, digging, excavation, trajectory.

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Authors: Zaiton Samdin, Kasimu Abdu Bakori, Hamimah Hassan

Abstract:

This paper attempts to investigate the factors that influence hotel managers- attitudes towards sustainable tourism practices (STP) in Kuala Lumpur and the state of Selangor in Malaysia. The study distributes 104 questionnaires to hotels ranging from one star to five-star categories including budget hotels. Out of this figure, 60 copies of the questionnaires were returned and analyzed. The finding revealed that of all the seven factors investigated, only the variables measuring incentives and knowledge have significantly influenced sustainable tourism practices in the country. Therefore, government and other green bodies within the country should continue to provide hotels with incentives for sound technologies. Moreover, the government agencies should continue to educate hoteliers on the relevance of environmental protection for the successful implementation of sustainable tourism practices.

Keywords: Attitude, incentive, knowledge, sustainable tourism practices

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

Abstract:

Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, Prediction, RBF neural network.

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Authors: Tatjana Eitrich, Bruno Lang

Abstract:

This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machine Training, Multi-ParameterKernels, Shared Memory Parallel Computing, Large Data

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Authors: Muhammad Rizwan Azhar, Emadadeen Ali

Abstract:

In this paper, periodic force operation of a wastewater treatment process has been studied for the improved process performance. A previously developed dynamic model for the process is used to conduct the performance analysis. The static version of the model was utilized first to determine the optimal productivity conditions for the process. Then, feed flow rate in terms of dilution rate i.e. (D) is transformed into sinusoidal function. Nonlinear model predictive control algorithm is utilized to regulate the amplitude and period of the sinusoidal function. The parameters of the feed cyclic functions are determined which resulted in improved productivity than the optimal productivity under steady state conditions. The improvement in productivity is found to be marginal and is satisfactory in substrate conversion compared to that of the optimal condition and to the steady state condition, which corresponds to the average value of the periodic function. Successful results were also obtained in the presence of modeling errors and external disturbances.

Keywords: Dilution rate, nonlinear model predictive control, sinusoidal function, wastewater treatment.

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Authors: M. Izadi, S. Hossainpour, D. Jalali-Vahid

Abstract:

Nanofluids are novel fluids that are going to have an important role in future industrial thermal device designs. Studies are being predominantly conducted on the mechanism of these heat transfers. The key to this attraction is in the increase in thermal conductivity brought about by the Nanofluids compared with the base fluid. Different models have been proposed for calculation of effective thermal conduction that has been gradually modified. In this investigation effect of nanolayer structure and Brownian motion of particles are studied and a new modified thermal conductivity model is proposed. Temperature, concentration, nanolayer thickness and particle size are taken as variables and their effect are studied simultaneously on the thermal conductivity of the fluids, showing the concentration of the nanoparticles to affect the nanolayer thickness which also affects the Brownian motion.

Keywords: Relative thermal conductivity, Brownian motion, Nanolayer structure.

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Authors: B. Kędra, R. Małkowski

Abstract:

This paper presents information on Power System Component Simulator – a device designed for LINTE^2 laboratory owned by Gdansk University of Technology in Poland. In this paper, we first provide an introductory information on the Power System Component Simulator and its capabilities. Then, the concept of the unit is presented. Requirements for the unit are described as well as proposed and introduced functions are listed. Implementation details are given. Hardware structure is presented and described. Information about used communication interface, data maintenance and storage solution, as well as used Simulink real-time features are presented. List and description of all measurements is provided. Potential of laboratory setup modifications is evaluated. Lastly, the results of experiments performed using Power System Component Simulator are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area.

Keywords: Power converter, Simulink real-time, MATLAB, load, tap controller.

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Authors: Laura Lukmanto, Harya Widiputra, Lukas

Abstract:

Studies in economics domain tried to reveal the correlation between stock markets. Since the globalization era, interdependence between stock markets becomes more obvious. The Dynamic Interaction Network (DIN) algorithm, which was inspired by a Gene Regulatory Network (GRN) extraction method in the bioinformatics field, is applied to reveal important and complex dynamic relationship between stock markets. We use the data of the stock market indices from eight countries around the world in this study. Our results conclude that DIN is able to reveal and model patterns of dynamic interaction from the observed variables (i.e. stock market indices). Furthermore, it is also found that the extracted network models can be utilized to predict movement of the stock market indices with a considerably good accuracy.

Keywords: complex dynamic relationship, dynamic interaction network, interactive stock markets, stock market interdependence.

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Authors: Ahmad Forouzantabar

Abstract:

this paper presents a novel neural network controller with composite adaptation low to improve the trajectory tracking problems of biped robots comparing with classical controller. The biped model has 5_link and 6 degrees of freedom and actuated by Plated Pneumatic Artificial Muscle, which have a very high power to weight ratio and it has large stoke compared to similar actuators. The proposed controller employ a stable neural network in to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitation of conventional controllers such as PD or adaptive controllers and guarantee good performance. This NN controller significantly improve the accuracy requirements by retraining the basic PD/PID loop, but adding an inner adaptive loop that allows the controller to learn unknown parameters such as friction coefficient, therefore improving tracking accuracy. Simulation results plus graphical simulation in virtual reality show that NN controller tracking performance is considerably better than PD controller tracking performance.

Keywords: Biped robot, Neural network, Plated Pneumatic Artificial Muscle, Composite adaptation

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Authors: Sánchez Acuña, Juan Camilo, Granados Gómez, Mildred Magaly, Navarrete Rodríguez, Luisa Fernanda

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Nowadays, the main biofuels source production as bioethanol is food crops. This means a high competition between foods and energy production. For this reason, it is necessary to take into account the use of new raw materials friendly to the environment. The main objective of this paper is to evaluate the potential of the agro-industrial banana crop residues in the production of bioethanol. A factorial design of 2⁴ was used, the design has variables such as pH, time and concentration of hydrolysis, another variable is the time of fermentation that is of 7 or 15 days. In the hydrolysis phase, the pH is acidic (H₂SO₄) or basic (NaOH), the time is 30 or 15 minutes and the concentration is 0.1 or 0.5 M. It was observed that basic media, low concentrations, fermentation, and higher pretreatment times produced better performance in terms of biofuel obtained.

Keywords: Bioethanol, biofuels, banana waste, hydrolysis.

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Authors: Joshi Manohar. V., Sujatha. P., Anjaneyulu K. S. R

Abstract:

The range of the output power is a very important and evident limitation of two-level inverters. In order to overcome this disadvantage, multilevel inverters are introduced. Recently, Cascade H-Bridge inverters have emerged as one of the popular converter topologies used in numerous industrial applications. The modulation switching strategies such as phase shifted carrier based Pulse Width Modulation (PWM) technique and Stair case modulation with Selective Harmonic Elimination (SHE) PWM technique are generally used. NR method is used to solve highly non linear transcendental equations which are formed by SHEPWM method. Generally NR method has a drawback of requiring good initial guess but in this paper a new approach is implemented for NR method with any random initial guess. A three phase CHB 11-level inverter is chosen for analysis. MATLAB/SIMULINK programming environment and harmonic profiles are compared. Finally this paper presents a method at fundamental switching frequency with least % THDV.

Keywords: Cascade H-bridge 11- level Inverter, NR method, Phase shifted carrier based pulse width modulation (PSCPWM), Selective Harmonic Elimination Pulse Width Modulation (SHEPWM), Total Harmonic Distortion (%THDv).

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Authors: Mohammad Javad Kargar, Fatemeh Azimzadeh

Abstract:

The vast amount of information on the World Wide Web is created and published by many different types of providers. Unlike books and journals, most of this information is not subject to editing or peer review by experts. This lack of quality control and the explosion of web sites make the task of finding quality information on the web especially critical. Meanwhile new facilities for producing web pages such as Blogs make this issue more significant because Blogs have simple content management tools enabling nonexperts to build easily updatable web diaries or online journals. On the other hand despite a decade of active research in information quality (IQ) there is no framework for measuring information quality on the Blogs yet. This paper presents a novel experimental framework for ranking quality of information on the Weblog. The results of data analysis revealed seven IQ dimensions for the Weblog. For each dimension, variables and related coefficients were calculated so that presented framework is able to assess IQ of Weblogs automatically.

Keywords: Information Quality, Weblog, Web Ranking, Web- Quality.

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Authors: John R. Rose, James P. Cleveland, Alvin Fox

Abstract:

Tandem mass spectrometry (MS/MS) is the engine driving high-throughput protein identification. Protein mixtures possibly representing thousands of proteins from multiple species are treated with proteolytic enzymes, cutting the proteins into smaller peptides that are then analyzed generating MS/MS spectra. The task of determining the identity of the peptide from its spectrum is currently the weak point in the process. Current approaches to de novo sequencing are able to compute candidate peptides efficiently. The problem lies in the limitations of current scoring functions. In this paper we introduce the concept of proteome signature. By examining proteins and compiling proteome signatures (amino acid usage) it is possible to characterize likely combinations of amino acids and better distinguish between candidate peptides. Our results strongly support the hypothesis that a scoring function that considers amino acid usage patterns is better able to distinguish between candidate peptides. This in turn leads to higher accuracy in peptide prediction.

Keywords: Tandem mass spectrometry, proteomics, scoring, peptide, de novo, mutual information

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Authors: H. Mahboubi, B. Moshiri, A. Khaki Seddigh

Abstract:

Recently, a great amount of interest has been shown in the field of modeling and controlling hybrid systems. One of the efficient and common methods in this area utilizes the mixed logicaldynamical (MLD) systems in the modeling. In this method, the system constraints are transformed into mixed-integer inequalities by defining some logic statements. In this paper, a system containing three tanks is modeled as a nonlinear switched system by using the MLD framework. Comparing the model size of the three-tank system with that of a two-tank system, it is deduced that the number of binary variables, the size of the system and its complexity tremendously increases with the number of tanks, which makes the control of the system more difficult. Therefore, methods should be found which result in fewer mixed-integer inequalities.

Keywords: Hybrid systems, mixed-integer inequalities, mixed logical dynamical systems, multi-tank system.

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Authors: Jonas Mayer, Kai-Frederic Seitz, Thorben Kuprat

Abstract:

In today’s competitive environment production’s logistic objectives such as ‘delivery reliability’ and ‘delivery time’ and distribution’s logistic objectives such as ‘service level’ and ‘delivery delay’ are attributed great importance. Especially for small and mid-sized enterprises (SME) attaining these objectives pose a key challenge. Within this context, one of the difficulties is that interactions between departments within the enterprise and their specific objectives are insufficiently taken into account and aligned. Interdepartmental independencies along with contradicting targets set within the different departments result in enterprises having sub-optimal logistic performance capability. This paper presents a research project which will systematically describe the interactions between departments and convert them into a quantifiable form.

Keywords: Department-specific actuating and control variables, interdepartmental interactions, cybernetic model, logistic objectives.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: Md. Emaj Uddin

Abstract:

Arrack is one of the forms of alcoholic beverage or liquor which is produced from palm or date juice and commonly consumed by the lower social class of all religious/ethnic communities in the north-western villages of Bangladesh. The purpose of the study was to compare arrack drinking patterns associated with socio-demographic status among the Muslim, Hindu, Santal, and Oraon communities in the Rasulpur union of Bangladesh. A total of 391 respondents (Muslim n-109, Hindu n-103, Santal n-89, Oraon n-90) selected by cluster random sampling were interviewed by ADP (Arrack Drinking Pattern) questionnaire. The results of Pearson Chi-Squire test revealed that arrack drinking patterns were significantly differed among the Muslim, Hindu, Santal, and Oraon communities- drinkers. In addition, the results of Spearman-s bivariate correlation coefficients also revealed that sociodemographic characteristics of the communities- drinkers were the significantly positive and negative associations with the arrack drinking patterns in the Rasulpur union, Bangladesh. The study suggests that further cross-cultural researches should be conducted on the consequences of arrack drinking patterns on the communities- drinkers.

Keywords: Arrack Drinking Patterns, Bangladesh, Community, Cross-Cultural Comparison, Socio-Demographic Status.

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Authors: H. Meradi, S. Bouhouche, M. Lahreche

Abstract:

In this work, we consider an application of neural networks in LD converter. Application of this approach assumes a reliable prediction of steel temperature and reduces a reblow ratio in steel work. It has been applied a conventional model to charge calculation, the obtained results by this technique are not always good, this is due to the process complexity. Difficulties are mainly generated by the noisy measurement and the process non linearities. Artificial Neural Networks (ANNs) have become a powerful tool for these complex applications. It is used a backpropagation algorithm to learn the neural nets. (ANNs) is used to predict the steel bath temperature in oxygen converter process for the end condition. This model has 11 inputs process variables and one output. The model was tested in steel work, the obtained results by neural approach are better than the conventional model.

Keywords: LD converter, bath temperature, neural networks.

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

Abstract:

This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: Concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC.

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Authors: F. Elashhab, L. Sheha, R. Fawzi Elsupikhe, A. E. A. Youssef, R. M. Sheltami, T. Alfazani

Abstract:

Chitosans, CSs, in solution are increasingly used in a range of geometric properties in various academic and industrial sectors, especially in the domain of pharmaceutical and biomedical engineering. In order to provide a tailoring guide of CSs to the applicants, gamma (γ)-irradiation technology and simple viscosity measurements have been used in this study. Accordingly, CS solid discs (0.5 cm thickness and 2.5 cm diameter) were exposed in air to Cobalt-60 (γ)-radiation, at room temperature and constant 50 kGy dose for different periods of exposer time (tγ). Diluted solutions of native and different irradiated CS were then prepared by dissolving 1.25 mg cm-3 of each polymer in 0.1 M NaCl/0.2 M CH3COOH. The single-concentration relative viscosity (ƞr) measurements were employed to obtain their intrinsic viscosity ([ƞ]) values and interrelated parameters, like: the molar mass (Mƞ), hydrodynamic radiuses (RH,ƞ), radius of gyration (RG,ƞ), and second virial coefficient (A2,ƞ) of CSs in the solution. The results show an exponential decrease of ƞr, [ƞ], Mƞ, RH,ƞ and RG,ƞ with increasing tγ. This suggests the influence of random chain-scission of CSs glycosidic bonds, with rate constant kr and kr-1 (lifetime τr ~ 0.017 min-1 and 57.14 min, respectively). The results also show an exponential decrease of A2ƞ with increasing tγ, which can be attributed to the growth of excluded volume effect in CS segments by tγ and, hence, better solution quality. The results are represented in following scaling laws as a tailoring guide to the applicants: RH,ƞ = 6.98 x 10-3 Mr0.65; RG,ƞ = 7.09 x 10-4 Mr0.83; A2,ƞ = 121.03 Mƞ,r-0.19.

Keywords: Gamma irradiation, geometric properties, kinetic model, scaling laws, viscosity measurement.

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Authors: Osuagwu Justine Ugochukwu, Sazali Abd Wahab, Sunday Sunday Akpan

Abstract:

Investigating the effects of transformational and transactional leadership styles on the performance of healthcare employees at the University Teaching Hospital (UNTH) in Enugu, Nigeria, was the goal of the research. The respondents were asked to fill out a structured questionnaire. The respondents were chosen using a straightforward random sampling technique and consisted of 370 health workers at the hospital. The result of the analysis revealed that transactional and transformational leadership style has a positive while ambidextrous leadership has a negative effect on healthcare workers' performance in UNTH, Enugu. Therefore, the management of public hospitals that have the capacity to change their top management approach to leadership styles will gain substantial support from their employees’ thereby increasing organizational commitment and performance among health workers. This will have remarkable social implications, one of which is a change in the work culture and attitude of medical personnel from the seemingly anti-community of patients to friendly engagement and treatment of patients leading to a harmonious coexistence among these individuals in society. Investigating ambidextrous leadership and the use of nonparametric analysis is unique and has brought knowledge to leadership literature.

Keywords: Workers’ performance, transformational leadership, transactional leadership, governance quality, ambidextrous leadership.

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Authors: Zixin Liu, Shu Lü, Shouming Zhong, Mao Ye

Abstract:

The problem of exponential stability and periodicity for a class of cellular neural networks (DCNNs) with time-varying delays is investigated. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via the methods of variation parameters and inequality techniques. These conditions are represented by some blocks of the interconnection matrices. Compared with some previous methods, the method used in this paper does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier criteria established in the literature cited therein. Two examples are discussed to illustrate the main results.

Keywords: Cellular neural networks, exponential stability, time varying delays, partitioned matrices, periodic solution.

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Authors: Dutescu Adriana, Amariei Corneliu, Sahlian Daniela, Stanila Aurelian, Stanila Oana

Abstract:

20 years of dentistry was a period of transition from communist to market economy but Romanian doctors have insufficient management knowledge. Recently, the need for modern management has increased due to technologies and superior materials appearance, as patient-s demands. Research goal is to increase efficiency by evaluating dental medical office cost categories in real pricing procedures. Empirical research is based on guided study that includes information about the association between categories of cost perception and therapeutic procedures commonly used in dental offices. Due to the obtained results to identify all the labours that make up a settled procedure costs were determined for each procedure. Financial evaluation software was created with the main functions: introducing and maintaining patient records, treatment and appointments made, procedures cost and monitoring office productivity. We believe that the study results can significantly improve the financial management of dental offices, increasing the effectiveness and quality of services.

Keywords: costs, financial methods, management.

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Authors: Ali Hajiha, Farhad Ghaffari, Nooshin Gholamali Tehrani

Abstract:

This study aims at investigating the empirical relationships between risk preference, internet preference, and internet knowledge which are known as user characteristics, in addition to perceived risk of the customers on the internet purchase intention. In order to test the relationships between the variables of model 174, a questionnaire was collected from the students with previous online experience. For the purpose of data analysis, confirmatory factor analysis (CFA) and structural equation model (SEM) was used. Test results show that the perceived risk affects the internet purchase intention, and increase or decrease of perceived risk influences the purchase intention when the customer does the internet shopping. Other factors such as internet preference, knowledge of the internet, and risk preference affect the internet purchase intention.

Keywords: Perceived risk, Internet preference, Internetknowledge, Risk preference, Internet purchase intention

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Authors: Z. Jannoo, B. W. Yap, N. Auchoybur, M. A. Lazim

Abstract:

The two common approaches to Structural Equation Modeling (SEM) are the Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM). There is much debate on the performance of CB-SEM and PLS-SEM for small sample size and when distributions are nonnormal. This study evaluates the performance of CB-SEM and PLS-SEM under normality and nonnormality conditions via a simulation. Monte Carlo Simulation in R programming language was employed to generate data based on the theoretical model with one endogenous and four exogenous variables. Each latent variable has three indicators. For normal distributions, CB-SEM estimates were found to be inaccurate for small sample size while PLS-SEM could produce the path estimates. Meanwhile, for a larger sample size, CB-SEM estimates have lower variability compared to PLS-SEM. Under nonnormality, CB-SEM path estimates were inaccurate for small sample size. However, CB-SEM estimates are more accurate than those of PLS-SEM for sample size of 50 and above. The PLS-SEM estimates are not accurate unless sample size is very large.  

Keywords: CB-SEM, Monte Carlo simulation, Normality conditions, Nonnormality, PLS-SEM.

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Authors: K. Bokreta, D. Benanaya

Abstract:

The objective of this study is to examine the relative effectiveness of monetary and fiscal policy in Algeria using the econometric modelling techniques of cointegration and vector error correction modelling to analyse and draw policy inferences. The chosen variables of fiscal policy are government expenditure and net taxes on products, while the effect of monetary policy is presented by the inflation rate and the official exchange rate. From the results, we find that in the long-run, the impact of government expenditures is positive, while the effect of taxes is negative on growth. Additionally, we find that the inflation rate is found to have little effect on GDP per capita but the impact of the exchange rate is insignificant. We conclude that fiscal policy is more powerful then monetary policy in promoting economic growth in Algeria.

Keywords: Economic growth, fiscal policy, monetary policy, VECM.

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Authors: Hakan Gürkan, Ahmet Denker

Abstract:

The Ministry of Defense (MoD) spends hundreds of millions of dollars on software to support its infrastructure, operate its weapons and provide command, control, communications, computing, intelligence, surveillance, and reconnaissance (C4ISR) functions. These and other all new advanced systems have a common critical component is information technology. Defense and Aerospace environment is continuously striving to keep up with increasingly sophisticated Information Technology (IT) in order to remain effective in today-s dynamic and unpredictable threat environment. This makes it one of the largest and fastest growing expenses of Defense. Hundreds of millions of dollars spent a year on IT projects. But, too many of those millions are wasted on costly mistakes. Systems that do not work properly, new components that are not compatible with old once, trendily new applications that do not really satisfy defense needs or lost though poorly managed contracts. This paper investigates and compiles the effective strategies that aim to end exasperation with low returns and high cost of Information Technology Acquisition for defense; it tries to show how to maximize value while reducing time and expenditure.

Keywords: Iterative Process, Acquisition Management, Project management, Software Economics, Requirement analysis.

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Authors: M. Bagci, H. Imrek, Omari M. Khalfan

Abstract:

Materials added to the matrix help improving operating properties of a composite. This experimental study has targeted to investigate this aim where Silicon Oxide particles were added to glass fibre and epoxy resin at an amount of 15% to the main material to obtain a sort of new composite material. Erosive wear behavior of epoxy-resin dipped composite materials reinforced with glass fibre and Silicon Oxide under three different impingement angles (30°, 60° and 90°), three different impact velocities (23, 34 and 53 m/s), two different angular Aluminum abrasive particle sizes (approximately 200 and 400 μm) and the fibre orientation of 45° (45/-45) were investigated. In the test results, erosion rates were obtained as functions of impingement angles, impact velocities, particle sizes and fibre orientation. Moreover, materials with addition of Silicon Oxide filler material exhibited lower wear as compared to neat materials with no added filler material. In addition, SEM views showing worn out surfaces of the test specimens were scrutinized.

Keywords: Erosive wear, fibre orientation, GF/EP, silicon oxide.

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

Abstract:

A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian Automotive Sector, Stock Market Decisions, Equity Portfolio Analysis, Decision Tree Classifiers, Statistical Data Analysis.

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Authors: N. Nacereddine, M. Tridi, S. S. Belaïfa, M. Zelmat

Abstract:

For the characterization of the weld defect region in the radiographic image, looking for features which are invariant regarding the geometrical transformations (rotation, translation and scaling) proves to be necessary because the same defect can be seen from several angles according to the orientation and the distance from the welded framework to the radiation source. Thus, panoply of geometrical attributes satisfying the above conditions is proposed and which result from the calculation of the geometrical parameters (surface, perimeter, etc.) on the one hand and the calculation of the different order moments, on the other hand. Because the large range in values of the raw features and taking into account other considerations imposed by some classifiers, the scaling of these values to lie between 0 and 1 is indispensable. The principal component analysis technique is used in order to reduce the number of the attribute variables in the aim to give better performance to the further defect classification.

Keywords: Geometric parameters, invariant attributes, principal component analysis, weld defect image.

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