Analysis and Measuring Surface Roughness of Nonwovens Using Machine Vision Method

Concerning the measurement of friction properties of textiles and fabrics using Kawabata Evaluation System (KES), whose output is constrained to the surface friction factor of fabric, and no other data would be generated; this research has been conducted to gain information about surface roughness regarding its surface friction factor. To assess roughness properties of light nonwovens, a 3-dimensional model of a surface has been simulated with regular sinuous waves through it as an ideal surface. A new factor was defined, namely Surface Roughness Factor, through comparing roughness properties of simulated surface and real specimens. The relation between the proposed factor and friction factor of specimens has been analyzed by regression, and results showed a meaningful correlation between them. It can be inferred that the new presented factor can be used as an acceptable criterion for evaluating the roughness properties of light nonwoven fabrics.

Soil-Vegetation Relationships in Arid Rangelands (Case Study: Nodushan Rangelands of Yazd, Iran)

The objective of this research was to identify the vegetation-soil relationships in Nodushan arid rangelands of Yazd. 5 sites were selected for measuring the cover of plant species and soil attributes. Soil samples were taken in 0-10 and 10-80 cm layers. The species studied were Salsola tomentosa, Salsola arbuscula, Peganum harmala, Zygophylum eurypterum and Eurotia ceratoides. Canonical correspondence analysis (CCA) was used to analyze the data. Based on the CCA results, 74.9 % of vegetation-soil variation was explained by axis 1-3. Axis 1, 2 and 3 accounted for 27.2%, 24.9 % and 22.8% of variance respectively. Correlation between axis 1, 2, 3 and speciesedaphic variables were 0.995, 0.989, 0.981 respectively. Soil texture, lime, salinity and organic matter significantly influenced the distribution of these plant species. Determination of soil-vegetation relationships will be useful for managing and improving rangelands in arid and semi arid environments.

A new Adaptive Approach for Histogram based Mouth Segmentation

The segmentation of mouth and lips is a fundamental problem in facial image analyisis. In this paper we propose a method for lip segmentation based on rg-color histogram. Statistical analysis shows, using the rg-color-space is optimal for this purpose of a pure color based segmentation. Initially a rough adaptive threshold selects a histogram region, that assures that all pixels in that region are skin pixels. Based on that pixels we build a gaussian model which represents the skin pixels distribution and is utilized to obtain a refined, optimal threshold. We are not incorporating shape or edge information. In experiments we show the performance of our lip pixel segmentation method compared to the ground truth of our dataset and a conventional watershed algorithm.

Wavelet Based Qualitative Assessment of Femur Bone Strength Using Radiographic Imaging

In this work, the primary compressive strength components of human femur trabecular bone are qualitatively assessed using image processing and wavelet analysis. The Primary Compressive (PC) component in planar radiographic femur trabecular images (N=50) is delineated by semi-automatic image processing procedure. Auto threshold binarization algorithm is employed to recognize the presence of mineralization in the digitized images. The qualitative parameters such as apparent mineralization and total area associated with the PC region are derived for normal and abnormal images.The two-dimensional discrete wavelet transforms are utilized to obtain appropriate features that quantify texture changes in medical images .The normal and abnormal samples of the human femur are comprehensively analyzed using Harr wavelet.The six statistical parameters such as mean, median, mode, standard deviation, mean absolute deviation and median absolute deviation are derived at level 4 decomposition for both approximation and horizontal wavelet coefficients. The correlation coefficient of various wavelet derived parameters with normal and abnormal for both approximated and horizontal coefficients are estimated. It is seen that in almost all cases the abnormal show higher degree of correlation than normals. Further the parameters derived from approximation coefficient show more correlation than those derived from the horizontal coefficients. The parameters mean and median computed at the output of level 4 Harr wavelet channel was found to be a useful predictor to delineate the normal and the abnormal groups.

Detection and Correction of Ectopic Beats for HRV Analysis Applying Discrete Wavelet Transforms

The clinical usefulness of heart rate variability is limited to the range of Holter monitoring software available. These software algorithms require a normal sinus rhythm to accurately acquire heart rate variability (HRV) measures in the frequency domain. Premature ventricular contractions (PVC) or more commonly referred to as ectopic beats, frequent in heart failure, hinder this analysis and introduce ambiguity. This investigation demonstrates an algorithm to automatically detect ectopic beats by analyzing discrete wavelet transform coefficients. Two techniques for filtering and replacing the ectopic beats from the RR signal are compared. One technique applies wavelet hard thresholding techniques and another applies linear interpolation to replace ectopic cycles. The results demonstrate through simulation, and signals acquired from a 24hr ambulatory recorder, that these techniques can accurately detect PVC-s and remove the noise and leakage effects produced by ectopic cycles retaining smooth spectra with the minimum of error.

Effect of Teaching Games for Understanding Approach on Students- Cognitive Learning Outcome

The study investigated the effects of Teaching Games for Understanding approach on students ‘cognitive learning outcome. The study was a quasi-experimental non-equivalent pretest-posttest control group design whereby 10 year old primary school students (n=72) were randomly assigned to an experimental and a control group. The experimental group students were exposed with TGfU approach and the control group with the Traditional Skill approach of handball game. Game Performance Assessment Instrument (GPAI) was used to measure students' tactical understanding and decision making in 3 versus 3 handball game situations. Analysis of covariance (ANCOVA) was used to analyze the data. The results reveal that there was a significant difference between the TGfU approach group and the traditional skill approach group students on post test score (F (1, 69) = 248.83, p < .05). The findings of this study suggested the importance of TGfU approach to improve primary students’ tactical understanding and decision making in handball game.

Modeling Stress-Induced Regulatory Cascades with Artificial Neural Networks

Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.

Numerical Simulation of CNT Incorporated Cement

Cement, the most widely used construction material is very brittle and characterized by low tensile strength and strain capacity. Macro to nano fibers are added to cement to provide tensile strength and ductility to it. Carbon Nanotube (CNT), one of the nanofibers, has proven to be a promising reinforcing material in the cement composites because of its outstanding mechanical properties and its ability to close cracks at the nano level. The experimental investigations for CNT reinforced cement is costly, time consuming and involves huge number of trials. Mathematical modeling of CNT reinforced cement can be done effectively and efficiently to arrive at the mechanical properties and to reduce the number of trials in the experiments. Hence, an attempt is made to numerically study the effective mechanical properties of CNT reinforced cement numerically using Representative Volume Element (RVE) method. The enhancement in its mechanical properties for different percentage of CNTs is studied in detail.

Vibration of Functionally Graded Cylindrical Shells Under Effect Clamped-Free Boundary Conditions Using Hamilton's Principle

In the present work, study of the vibration of thin cylindrical shells made of a functionally gradient material (FGM) composed of stainless steel and nickel is presented. Material properties are graded in the thickness direction of the shell according to volume fraction power law distribution. The objective is to study the natural frequencies, the influence of constituent volume fractions and the effects of boundary conditions on the natural frequencies of the FG cylindrical shell. The study is carried out using third order shear deformation shell theory. The analysis is carried out using Hamilton's principle. The governing equations of motion of FG cylindrical shells are derived based on shear deformation theory. Results are presented on the frequency characteristics, influence of constituent volume fractions and the effects of clamped-free boundary conditions

Tagging by Combining Rules- Based Method and Memory-Based Learning

Many natural language expressions are ambiguous, and need to draw on other sources of information to be interpreted. Interpretation of the e word تعاون to be considered as a noun or a verb depends on the presence of contextual cues. To interpret words we need to be able to discriminate between different usages. This paper proposes a hybrid of based- rules and a machine learning method for tagging Arabic words. The particularity of Arabic word that may be composed of stem, plus affixes and clitics, a small number of rules dominate the performance (affixes include inflexional markers for tense, gender and number/ clitics include some prepositions, conjunctions and others). Tagging is closely related to the notion of word class used in syntax. This method is based firstly on rules (that considered the post-position, ending of a word, and patterns), and then the anomaly are corrected by adopting a memory-based learning method (MBL). The memory_based learning is an efficient method to integrate various sources of information, and handling exceptional data in natural language processing tasks. Secondly checking the exceptional cases of rules and more information is made available to the learner for treating those exceptional cases. To evaluate the proposed method a number of experiments has been run, and in order, to improve the importance of the various information in learning.

Markov Chain Monte Carlo Model Composition Search Strategy for Quantitative Trait Loci in a Bayesian Hierarchical Model

Quantitative trait loci (QTL) experiments have yielded important biological and biochemical information necessary for understanding the relationship between genetic markers and quantitative traits. For many years, most QTL algorithms only allowed one observation per genotype. Recently, there has been an increasing demand for QTL algorithms that can accommodate more than one observation per genotypic distribution. The Bayesian hierarchical model is very flexible and can easily incorporate this information into the model. Herein a methodology is presented that uses a Bayesian hierarchical model to capture the complexity of the data. Furthermore, the Markov chain Monte Carlo model composition (MC3) algorithm is used to search and identify important markers. An extensive simulation study illustrates that the method captures the true QTL, even under nonnormal noise and up to 6 QTL.

Biometric Methods and Implementation of Algorithms

Biometric measures of one kind or another have been used to identify people since ancient times, with handwritten signatures, facial features, and fingerprints being the traditional methods. Of late, Systems have been built that automate the task of recognition, using these methods and newer ones, such as hand geometry, voiceprints and iris patterns. These systems have different strengths and weaknesses. This work is a two-section composition. In the starting section, we present an analytical and comparative study of common biometric techniques. The performance of each of them has been viewed and then tabularized as a result. The latter section involves the actual implementation of the techniques under consideration that has been done using a state of the art tool called, MATLAB. This tool aids to effectively portray the corresponding results and effects.

Relational Impact of Job Stress on Gender Based Managerial Effectiveness in Ghanaian Organizations

This study explored the relationship between occupational stress and the perceived effectiveness of men and women managers in Ghanaian organizations. The exploration is underlined by attempt to understand the degree to which male and female managers in Ghanaian organizations experience occupational stress at the workplace. The purpose is to examine the sources and extents of occupational stress experienced by male and female managers in Ghana. Data was collected using questionnaires and analyzed using both descriptive statistics and correlation analysis. The results showed that female managers in Ghana are more likely to report of more stress experiences in the workplace than their male counterparts. The female managers are more likely to perceive role conflict and alienation as job stressors while the male managers perceived blocked career as a major source of workplace stress. It is concluded that despite the female managers experiencing enormous level of occupational stress, there was no significant differences between their managerial effectiveness and that of the male.

Intensity of Singular Stress Field at the Corner of Adhesive Layer in Bonded Plate

In this paper the strength of adhesive joint under tension and bending is discussed on the basis of intensity of singular stress by the application of FEM. A useful method is presented with focusing on the stress at the edge of interface between the adhesive and adherent obtained by FEM. After analyzing the adhesive joint strength with all material combinations, it is found that to improve the interface strength, thin adhesive layers are desirable because the intensity of singular stress decreases with decreasing the thickness.

Interoperability in Component Based Software Development

The ability of information systems to operate in conjunction with each other encompassing communication protocols, hardware, software, application, and data compatibility layers. There has been considerable work in industry on the development of component interoperability models, such as CORBA, (D)COM and JavaBeans. These models are intended to reduce the complexity of software development and to facilitate reuse of off-the-shelf components. The focus of these models is syntactic interface specification, component packaging, inter-component communications, and bindings to a runtime environment. What these models lack is a consideration of architectural concerns – specifying systems of communicating components, explicitly representing loci of component interaction, and exploiting architectural styles that provide well-understood global design solutions. The development of complex business applications is now focused on an assembly of components available on a local area network or on the net. These components must be localized and identified in terms of available services and communication protocol before any request. The first part of the article introduces the base concepts of components and middleware while the following sections describe the different up-todate models of communication and interaction and the last section shows how different models can communicate among themselves.

Land Use Changes in Two Mediterranean Coastal Regions: Do Urban Areas Matter?

This paper focuses on Land Use and Land Cover Changes (LULCC) occurred in the urban coastal regions of the Mediterranean basin in the last thirty years. LULCC were assessed diachronically (1975-2006) in two urban areas, Rome (Italy) and Athens (Greece), by using CORINE land cover maps. In strictly coastal territories a persistent growth of built-up areas at the expenses of both agricultural and forest land uses was found. On the contrary, a different pattern was observed in the surrounding inland areas, where a high conversion rate of the agricultural land uses to both urban and forest land uses was recorded. The impact of city growth on the complex pattern of coastal LULCC is finally discussed.

Mathematical Programming on Multivariate Calibration Estimation in Stratified Sampling

Calibration estimation is a method of adjusting the original design weights to improve the survey estimates by using auxiliary information such as the known population total (or mean) of the auxiliary variables. A calibration estimator uses calibrated weights that are determined to minimize a given distance measure to the original design weights while satisfying a set of constraints related to the auxiliary information. In this paper, we propose a new multivariate calibration estimator for the population mean in the stratified sampling design, which incorporates information available for more than one auxiliary variable. The problem of determining the optimum calibrated weights is formulated as a Mathematical Programming Problem (MPP) that is solved using the Lagrange multiplier technique.

Information System Life Cycle: Applications in Construction and Manufacturing

In this paper, we present the information life cycle, and analyze the importance of managing the corporate application portfolio across this life cycle. The approach presented here does not correspond just to the extension of the traditional information system development life cycle. This approach is based in the generic life cycle employed in other contexts like manufacturing or marketing. In this paper it is proposed a model of an information system life cycle, supported in the assumption that a system has a limited life. But, this limited life may be extended. This model is also applied in several cases; being reported here two examples of the framework application in a construction enterprise, and in a manufacturing enterprise.

Authentication and Data Hiding Using a Reversible ROI-based Watermarking Scheme for DICOM Images

In recent years image watermarking has become an important research area in data security, confidentiality and image integrity. Many watermarking techniques were proposed for medical images. However, medical images, unlike most of images, require extreme care when embedding additional data within them because the additional information must not affect the image quality and readability. Also the medical records, electronic or not, are linked to the medical secrecy, for that reason, the records must be confidential. To fulfill those requirements, this paper presents a lossless watermarking scheme for DICOM images. The proposed a fragile scheme combines two reversible techniques based on difference expansion for patient's data hiding and protecting the region of interest (ROI) with tamper detection and recovery capability. Patient's data are embedded into ROI, while recovery data are embedded into region of non-interest (RONI). The experimental results show that the original image can be exactly extracted from the watermarked one in case of no tampering. In case of tampered ROI, tampered area can be localized and recovered with a high quality version of the original area.

Analytical Model for Brine Discharges from a Sea Outfall with Multiport Diffusers

Multiport diffusers are the effective engineering devices installed at the modern marine outfalls for the steady discharge of effluent streams from the coastal plants, such as municipal sewage treatment, thermal power generation and seawater desalination. A mathematical model using a two-dimensional advection-diffusion equation based on a flat seabed and incorporating the effect of a coastal tidal current is developed to calculate the compounded concentration following discharges of desalination brine from a sea outfall with multiport diffusers. The analytical solutions are computed graphically to illustrate the merging of multiple brine plumes in shallow coastal waters, and further approximation will be made to the maximum shoreline's concentration to formulate dilution of a multiport diffuser discharge.