The Role of Knowledge Management in Enterprise 2.0

The term Enterprise 2.0 (E2.0) describes a collection of organizational and IT practices that help organizations establish flexible work models, visible knowledge-sharing practices, and higher levels of community participation. E2.0 parallels and builds on another term commonly being used in the industry – Web 2.0. E2.0 represents also new packaging for strategic collaboration and Knowledge Management (KM). Organizations rely on collaboration and KM initiatives to attain innovation, growth, productivity, and performance goals.

Use of Novel Algorithms MAJE4 and MACJER-320 for Achieving Confidentiality and Message Authentication in SSL and TLS

Extensive use of the Internet coupled with the marvelous growth in e-commerce and m-commerce has created a huge demand for information security. The Secure Socket Layer (SSL) protocol is the most widely used security protocol in the Internet which meets this demand. It provides protection against eaves droppings, tampering and forgery. The cryptographic algorithms RC4 and HMAC have been in use for achieving security services like confidentiality and authentication in the SSL. But recent attacks against RC4 and HMAC have raised questions in the confidence on these algorithms. Hence two novel cryptographic algorithms MAJE4 and MACJER-320 have been proposed as substitutes for them. The focus of this work is to demonstrate the performance of these new algorithms and suggest them as dependable alternatives to satisfy the need of security services in SSL. The performance evaluation has been done by using practical implementation method.

Flow and Heat Transfer of a Nanofluid over a Shrinking Sheet

The problem of laminar fluid flow which results from the shrinking of a permeable surface in a nanofluid has been investigated numerically. The model used for the nanofluid incorporates the effects of Brownian motion and thermophoresis. A similarity solution is presented which depends on the mass suction parameter S, Prandtl number Pr, Lewis number Le, Brownian motion number Nb and thermophoresis number Nt. It was found that the reduced Nusselt number is decreasing function of each dimensionless number.

A State Aggregation Approach to Singularly Perturbed Markov Reward Processes

In this paper, we propose a single sample path based algorithm with state aggregation to optimize the average rewards of singularly perturbed Markov reward processes (SPMRPs) with a large scale state spaces. It is assumed that such a reward process depend on a set of parameters. Differing from the other kinds of Markov chain, SPMRPs have their own hierarchical structure. Based on this special structure, our algorithm can alleviate the load in the optimization for performance. Moreover, our method can be applied on line because of its evolution with the sample path simulated. Compared with the original algorithm applied on these problems of general MRPs, a new gradient formula for average reward performance metric in SPMRPs is brought in, which will be proved in Appendix, and then based on these gradients, the schedule of the iteration algorithm is presented, which is based on a single sample path, and eventually a special case in which parameters only dominate the disturbance matrices will be analyzed, and a precise comparison with be displayed between our algorithm with the old ones which is aim to solve these problems in general Markov reward processes. When applied in SPMRPs, our method will approach a fast pace in these cases. Furthermore, to illustrate the practical value of SPMRPs, a simple example in multiple programming in computer systems will be listed and simulated. Corresponding to some practical model, physical meanings of SPMRPs in networks of queues will be clarified.

Managing Meat Safety at South African Abattoirs

The importance of ensuring safe meat handling and processing practices has been demonstrated in global reports on food safety scares and related illness and deaths. This necessitated stricter meat safety control strategies. Today, many countries have regulated towards preventative and systematic control over safe meat processing at abattoirs utilizing the Hazard Analysis Critical Control Point (HACCP) principles. HACCP systems have been reported as effective in managing food safety risks, if correctly implemented. South Africa has regulated the Hygiene Management System (HMS) based on HACCP principles applicable to abattoirs. Regulators utilise the Hygiene Assessment System (HAS) to audit compliance at abattoirs. These systems were benchmarked from the United Kingdom (UK). Little research has been done them since inception as of 2004. This paper presents a review of the two systems, its implementation and comparison with HACCP. Recommendations are made for future research to demonstrate the utility of the HMS and HAS in assuring safe meat to consumers.

The Nanobiotechnology of Obtaining of Collagen Gels from Marin Fish Skin and Yours Reological Properties for using Like New Materials in Dental Medicine

This paper aims at presenting the biotechnology used to obtain collagen-based gels from shark (Squalus acanthias) and brill skin, marine fish growing in the Black Sea. Due to the structure of its micro-fibres, collagen can be considered a nanomaterial; in order to use collagen-based matrixes as biomaterial, rheological studies must be performed first, to state whether they are stable or not. For the triple-helix structure to remain stable within these gels at room or human body temperature, they must be stabilized by reticulation.

Manual Testing of Web Software Systems Supported by Direct Guidance of the Tester Based On Design Model

Software testing is important stage of software development cycle. Current testing process involves tester and electronic documents with test case scenarios. In this paper we focus on new approach to testing process using automated test case generation and tester guidance through the system based on the model of the system. Test case generation and model-based testing is not possible without proper system model. We aim on providing better feedback from the testing process thus eliminating the unnecessary paper work.

Prioritizing Service Quality Dimensions:A Neural Network Approach

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.

Relationship among Leisure Satisfaction, Spiritual Wellness, and Self-Esteem of Older Adults

This study sought to determine whether there were relationships existed among leisure satisfaction, self-esteem, and spiritual wellness. Four hundred survey instruments were distributed, and 334 effective instruments were returned, for an effective rate of 83.5%. The participants were recruited from a purposive sampling that subjects were at least 60 years of age and retired in Tainan City, Taiwan. Three instruments were used in this research: Leisure Satisfaction Scale (LSS), Self-Esteem Scale (SES), and Spirituality Assessment Scale (SAS). The collected data were analyzed statistically. The findings of this research were as follows: 1. There is significantly correlated between leisure satisfaction and spiritual wellness. 2. There is significantly correlated between leisure satisfaction and self-esteem. 3. There is significantly correlated between spiritual wellness and self-esteem.

Computer Modeling of Drug Distribution after Intravitreal Administration

Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.

Transmission Model for Plasmodium Vivax Malaria: Conditions for Bifurcation

Plasmodium vivax malaria differs from P. falciparum malaria in that a person suffering from P. vivax infection can suffer relapses of the disease. This is due the parasite being able to remain dormant in the liver of the patients where it is able to re-infect the patient after a passage of time. During this stage, the patient is classified as being in the dormant class. The model to describe the transmission of P. vivax malaria consists of a human population divided into four classes, the susceptible, the infected, the dormant and the recovered. The effect of a time delay on the transmission of this disease is studied. The time delay is the period in which the P. vivax parasite develops inside the mosquito (vector) before the vector becomes infectious (i.e., pass on the infection). We analyze our model by using standard dynamic modeling method. Two stable equilibrium states, a disease free state E0 and an endemic state E1, are found to be possible. It is found that the E0 state is stable when a newly defined basic reproduction number G is less than one. If G is greater than one the endemic state E1 is stable. The conditions for the endemic equilibrium state E1 to be a stable spiral node are established. For realistic values of the parameters in the model, it is found that solutions in phase space are trajectories spiraling into the endemic state. It is shown that the limit cycle and chaotic behaviors can only be achieved with unrealistic parameter values.

Hierarchical PSO-Adaboost Based Classifiers for Fast and Robust Face Detection

We propose a fast and robust hierarchical face detection system which finds and localizes face images with a cascade of classifiers. Three modules contribute to the efficiency of our detector. First, heterogeneous feature descriptors are exploited to enrich feature types and feature numbers for face representation. Second, a PSO-Adaboost algorithm is proposed to efficiently select discriminative features from a large pool of available features and reinforce them into the final ensemble classifier. Compared with the standard exhaustive Adaboost for feature selection, the new PSOAdaboost algorithm reduces the training time up to 20 times. Finally, a three-stage hierarchical classifier framework is developed for rapid background removal. In particular, candidate face regions are detected more quickly by using a large size window in the first stage. Nonlinear SVM classifiers are used instead of decision stump functions in the last stage to remove those remaining complex nonface patterns that can not be rejected in the previous two stages. Experimental results show our detector achieves superior performance on the CMU+MIT frontal face dataset.

Institutionalising Corporate Social Responsibility: A Study on the CSR Statements on Corporate Websites of Malaysian and Singapore Corporations

The purpose of this paper is to examine the current state of corporate social responsibility statements on corporate websites of Malaysian and Singaporean corporations and analyze how the CSR statements contribute in building a unique corporate identity of corporations. Content analysis is employed to examine the websites of Malaysian and Singaporean consumer corporations. It is believed that generally most companies tend to publish and communicate their CSR statements visibly to general stakeholders. However, there is a significantly different outcome of the articulation of CSR on practices on websites between Malaysian and Singaporean consumer corporations. A number of Singaporean organizations were found less concerned with CSR practices as compared to Malaysian organizations. The findings indicate a need for corporations in Malaysia and Singapore to orchestrate their core competence of CSR activities in order to develop a unique corporate identity in a global business environment.

A New Spectral-based Approach to Query-by-Humming for MP3 Songs Database

In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.

Electronic System Design for Respiratory Signal Processing

This paper presents the design related to the electronic system design of the respiratory signal, including phases for processing, followed by the transmission and reception of this signal and finally display. The processing of this signal is added to the ECG and temperature sign, put up last year. Under this scheme is proposed that in future also be conditioned blood pressure signal under the same final printed circuit and worked.

Data Oriented Model of Image: as a Framework for Image Processing

This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.

Anomaly Based On Frequent-Outlier for Outbreak Detection in Public Health Surveillance

Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

On-line Speech Enhancement by Time-Frequency Masking under Prior Knowledge of Source Location

This paper presents the source extraction system which can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.

Curing Methods Yield Multiple Refractive Index of Benzocyclobutene Polymer Film

Refractive index control of benzocyclobutene (BCB 4024-40) is achieved by facilitating different conditions during the thermal curing of BCB film. Refractive index (RI) change of 1.49% is obtained with curing of BCB film using an oven, while the RI change is 0.1% when the BCB is cured using a hotplate. The two different curing methods exhibit a temperature dependent refractive index change of the BCB photosensitive polymer. By carefully controlling the curing conditions, multiple layers of BCB with different RI can be fabricated, which can then be applied in the fabrication of optical waveguides.

Coastal Ecological Sensitivity and Risk Assessment: A Case Study of Sea Level Change in Apodi River (Atlantic Ocean), Northeast Brazil

The present study has been carried out with a view to calculate the coastal vulnerability index (CVI) to know the high and low sensitive areas and area of inundation due to future SLR. Both conventional and remotely sensed data were used and analyzed through the modelling technique. Out of the total study area, 8.26% is very high risk, 14.21% high, 9.36% medium, 22.46% low and 7.35% in the very low vulnerable category, due to costal components. Results of the inundation analysis indicate that 225.2 km² and 397 km² of the land area will be submerged by flooding at 1m and 10m inundation levels. The most severely affected sectors are expected to be the residential, industrial and recreational areas. As this coast is planned for future coastal developmental activities, measures such as industrializations, building regulation, urban growth planning and agriculture, development of an integrated coastal zone management, strict enforcement of the Coastal Regulation Zone (CRZ) Act, monitoring of impacts and further research in this regard are recommended for the study area.