Verification Process of Cylindrical Contact Force Models for Internal Contact Modeling

In the numerical solution of the forward dynamics of a multibody system, the positions and velocities of the bodies in the system are obtained first. With the information of the system state variables at each time step, the internal and external forces acting on the system are obtained by appropriate contact force models if the continuous contact method is used instead of a discrete contact method. The local deformation of the bodies in contact, represented by penetration, is used to compute the contact force. The ability and suitability with current cylindrical contact force models to describe the contact between bodies with cylindrical geometries with particular focus on internal contacting geometries involving low clearances and high loads simultaneously is discussed in this paper. A comparative assessment of the performance of each model under analysis for different contact conditions, in particular for very different penetration and clearance values, is presented. It is demonstrated that some models represent a rough approximation to describe the conformal contact between cylindrical geometries because contact forces are underestimated.

Investigation of Increasing the Heat Transfer from Flat Surfaces Using Boundary Layer Excitation

The present study is concerned with effect of exciting boundary layer on increase in heat transfer from flat surfaces. As any increase in heat transfer between a fluid inside a face and another one outside of it can cause an increase in some equipment's efficiency, so at this present we have tried to increase the wall's heat transfer coefficient by exciting the fluid boundary layer. By a collision between flow and the placed block at the fluid way, the flow pattern and the boundary layer stability will change. The flow way inside the channel is simulated as a 2&3-dimensional channel by Gambit TM software. With studying the achieved results by this simulation for the flow way inside the channel with a block coordinating with Fluent TM software, it's determined that the figure and dimensions of the exciter are too important for exciting the boundary layer so that any increase in block dimensions in vertical side against the flow and any reduction in its dimensions at the flow side can increase the average heat transfer coefficient from flat surface and increase the flow pressure loss. Using 2&3-dimensional analysis on exciting the flow at the flow way inside a channel by cylindrical block at the same time with the external flow, we came to this conclusion that the heat flux transferred from the surface, is increased considerably in terms of the condition without excitation. Also, the k-e turbulence model is used.

Biological Data Integration using SOA

Nowadays scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data accessing, analyzing, and visualization tools. This research suggests the use of Service Oriented Architecture (SOA) to integrate biological data from different data sources. This work shows SOA will solve the problems that facing integration process and if the biologist scientists can access the biological data in easier way. There are several methods to implement SOA but web service is the most popular method. The Microsoft .Net Framework used to implement proposed architecture.

Region Based Hidden Markov Random Field Model for Brain MR Image Segmentation

In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.

Offline Signature Recognition using Radon Transform

In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.

Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification

In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources.

Performance Improvement in the Bivariate Models by using Modified Marginal Variance of Noisy Observations for Image-Denoising Applications

Most simple nonlinear thresholding rules for wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper compares different Shrinkage functions used for image-denoising. The second part of the paper compares different bivariate models and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill, Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values are calculated for each noise level.

Parallel Explicit Group Domain Decomposition Methods for the Telegraph Equation

In a previous work, we presented the numerical solution of the two dimensional second order telegraph partial differential equation discretized by the centred and rotated five-point finite difference discretizations, namely the explicit group (EG) and explicit decoupled group (EDG) iterative methods, respectively. In this paper, we utilize a domain decomposition algorithm on these group schemes to divide the tasks involved in solving the same equation. The objective of this study is to describe the development of the parallel group iterative schemes under OpenMP programming environment as a way to reduce the computational costs of the solution processes using multicore technologies. A detailed performance analysis of the parallel implementations of points and group iterative schemes will be reported and discussed.

An Efficient Algorithm for Motion Detection Based Facial Expression Recognition using Optical Flow

One of the popular methods for recognition of facial expressions such as happiness, sadness and surprise is based on deformation of facial features. Motion vectors which show these deformations can be specified by the optical flow. In this method, for detecting emotions, the resulted set of motion vectors are compared with standard deformation template that caused by facial expressions. In this paper, a new method is introduced to compute the quantity of likeness in order to make decision based on the importance of obtained vectors from an optical flow approach. For finding the vectors, one of the efficient optical flow method developed by Gautama and VanHulle[17] is used. The suggested method has been examined over Cohn-Kanade AU-Coded Facial Expression Database, one of the most comprehensive collections of test images available. The experimental results show that our method could correctly recognize the facial expressions in 94% of case studies. The results also show that only a few number of image frames (three frames) are sufficient to detect facial expressions with rate of success of about 83.3%. This is a significant improvement over the available methods.

The Issues of Effectiveness of Advertisement Communication Process: A Case Study of Lithuania Consumers

The goal of this study was to disclose the core of the advertising research based on the psychological aspects by acquainting with the nature of advertising research and revealing the importance of psychological aspects of advertising during the advertising research. The growing interest in consumer response to advertisement served as an encouragement to make the analysis of psychological aspects of the advertising research, because the information obtained during the advertising research helps to answer the question how advertising really works. In the research analysis focuses on the nature of advertising research. The place of advertising research in advertisement planning process and the advertising research process are unfolded. Moreover, the importance of psychological aspects in the advertising research is being examined. The certain psychological aspects like the particularities of advertising communication process, psychological process that are active at advertising acceptance and awareness process as well as the advertising effects are analysed in more detail.

Deficiencies of Lung Segmentation Techniques using CT Scan Images for CAD

Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the dataset of 19 patients consisting of a total of 917 images. We obtained datasets of 11 patients from Ackron University, USA and of 8 patients from AGA Khan Medical University, Pakistan. After testing the algorithms against datasets, the deficiencies of each algorithm have been highlighted.

Effective Methodology for Security Risk Assessment of Computer Systems

Today, computer systems are more and more complex and support growing security risks. The security managers need to find effective security risk assessment methodologies that allow modeling well the increasing complexity of current computer systems but also maintaining low the complexity of the assessment procedure. This paper provides a brief analysis of common security risk assessment methodologies leading to the selection of a proper methodology to fulfill these requirements. Then, a detailed analysis of the most effective methodology is accomplished, presenting numerical examples to demonstrate how easy it is to use.

Biodegradable Surfactants for Advanced Drug Delivery Strategies

Oxidative stress makes up common incidents in eukaryotic metabolism. The presence of diverse components disturbing the equilibrium during oxygen metabolism increases oxidative damage unspecifically in living cells. Body´s own ubiquinone (Q10) seems to be a promising drug in defending the heightened appearance of reactive oxygen species (ROS). Though, its lipophilic properties require a new strategy in drug formulation to overcome their low bioavailability. Consequently, the manufacture of heterogeneous nanodispersions is in focus for medical applications. The composition of conventional nanodispersions is made up of a drug-consisting core and a surfactive agent, also named as surfactant. Long-termed encapsulation of the surfactive components into tissues might be the consequence of the use during medical therapeutics. The potential of provoking side-effects is given by their nonbiodegradable properties. Further improvements during fabrication process use the incorporation of biodegradable components such as modified γ-polyglutamic acid which decreases the potential of prospective side-effects.

Fault Detection of Broken Rotor Bars Using Stator Current Spectrum for the Direct Torque Control Induction Motor

The numerous qualities of squirrel cage induction machines enhance their use in industry. However, various faults can occur, such as stator short-circuits and rotor failures. In this paper, we use a technique based on the spectral analysis of stator current in order to detect the fault in the machine: broken rotor bars. Thus, the number effect of the breaks has been highlighted. The effect is highlighted by considering the machine controlled by the Direct Torque Control (DTC). The key to fault detection is the development of a simplified dynamic model of a squirrel cage induction motor taking account the broken bars fault and the stator current spectrum analysis (FFT).

The Story of Mergers and Acquisitions: Using Narrative Theory to Understand the Uncertainty of Organizational Change

This paper examines the influence of communication form on employee uncertainty during mergers and acquisitions (M&As). Specifically, the author uses narrative theory to analyze how narrative organizational communication affects the three components of uncertainty – decreased predictive, explanatory, and descriptive ability. It is hypothesized that employees whose organizations use narrative M&A communication will have greater predictive, explanatory, and descriptive abilities than employees of organizations using non-narrative M&A communication. This paper contributes to the stream of research examining uncertainty during mergers and acquisitions and argues that narratives are an effective means of managing uncertainty in the mergers and acquisitions context.

Enhancing the Quality of Learning by Using an Innovative Approach for Teaching Energy in Secondary Schools

This paper presents the results of the authors in designing, experimenting, assessing and transferring an innovative approach to energy education in secondary schools, aimed to enhance the quality of learning in terms of didactic curricula and pedagogic methods. The training is online delivered to youngsters via e-Books and portals specially designed for this purpose or by learning by doing via interactive games. An online educational methodology is available teachers.

Application of Fluorescent Pseudomonads Inoculant Formulations on Vigna mungo through Field Trial

Vermiculite was used to develop inorganic carrier-based formulations of fluorescent pseudomonad strains R62 and R81. The effect of bio-inoculation of fluorescent pseudomonad strains R62 and R81 (plant growth promoting and biocontrol agent) on growth responses of Vigna-mungo under field condition was enumerated. The combined bioinoculation of these two organisms in a formuation increased the pods yield by 300% in comparison to the control crop. There was also significant increment in the other plant growth responses such as dry root weight, dry shoot weight, shoot length and number of branches per plant.

Supply Chain Management and E-Commerce Technology Adoption among Logistics Service Providers in Malaysia

Logistics is part of the supply chain processes that plans, implements, and controls the efficient and effective forward and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customer requirements. This research aims to investigate the current status and future direction of the use of Information Technology (IT) for logistics, focusing on Supply Chain Management (SCM) and E-Commerce adoption in Malaysia. Therefore, this research stresses on the type of technology being adopted, factors, benefits and barriers affecting the innovation in SCM and E-Commerce technology adoption among Logistics Service Providers (LSP). A mailed questionnaire survey was conducted to collect data from 265 logistics companies in Johor. The research revealed a high level of SCM technology adoption among LSP as they had adopted SCM technology in various business processes while they perceived a high level of benefits from SCM adoption.

Steady-State Analysis and Control of Double Feed Induction Motor

This paper explores steady-state characteristics of grid-connected doubly fed induction motor (DFIM) in case of unity power factor operation. Based on the synchronized mathematical model, analytic determination of the control laws is presented and illustrated by various figures to understand the effect of the applied rotor voltage on the speed and the active power. On other hand, unlike previous works where the stator resistance was neglected, in this work, stator resistance is included such that the equations can be applied to small wind turbine generators which are becoming more popular. Finally the work is crowned by integration of the studied induction generator in a wind system where an open loop control is proposed confers a remarkable simplicity of implementation compared to the known methods.