Supercritical Fluid Extraction of Lutein Esters from Marigold Flowers and their Hydrolysis by Improved Saponification and Enzyme Biocatalysis

Lutein is a dietary oxycarotenoid which is found to reduce the risks of Age-related Macular Degeneration (AMD). Supercritical fluid extraction of lutein esters from marigold petals was carried out and was found to be much effective than conventional solvent extraction. The saponification of pre-concentrated lutein esters to produce free lutein was studied which showed a composition of about 88% total carotenoids (UV-VIS spectrophotometry) and 90.7% lutein (HPLC). The lipase catalyzed hydrolysis of lutein esters in conventional medium was investigated. The optimal temperature, pH, enzyme concentration and water activity were found to be 50°C, 7, 15% and 0.33 respectively and the activity loss of lipase was about 25% after 8 times re-use in at 50°C for 12 days. However, the lipase catalyzed hydrolysis of lutein esters in conventional media resulted in poor conversions (16.4%).

Information Support for Emergency Staff Processes and Effective Decisions

Managing the emergency situations at the Emergency Staff requires a high co-operation between its members and their fast decision making. For these purpose it is necessary to prepare Emergency Staff members adequately. The aim of this paper is to describe the development of information support that focuses to emergency staff processes and effective decisions. The information support is based on the principles of process management, and Process Framework for Emergency Management was used during the development. The output is the information system that allows users to simulate an emergency situation, including effective decision making. The system also evaluates the progress of the emergency processes solving by quantitative and qualitative indicators. By using the simulator, a higher quality education of specialists can be achieved. Therefore, negative impacts resulting from arising emergency situations can be directly reduced.

Design the Bowtie Antenna for the Detection of the Tumor in Microwave Tomography

Early breast cancer detection is an emerging field of research as it can save the women infected by malignant tumors. Microwave breast imaging is based on the electrical property contrast between healthy and malignant tumor. This contrast can be detected by use of microwave energy with an array of antennas that illuminate the breast through coupling medium and by measuring the scattered fields. In this paper, author has been presented the design and simulation results of the bowtie antenna. This bowtie antenna is designed for the detection of breast cancer detection.

Financial Regulations in the Process of Global Financial Crisis and Macroeconomics Impact of Basel III

Basel III (or the Third Basel Accord) is a global regulatory standard on bank capital adequacy, stress testing and market liquidity risk agreed upon by the members of the Basel Committee on Banking Supervision in 2010-2011, and scheduled to be introduced from 2013 until 2018. Basel III is a comprehensive set of reform measures. These measures aim to; (1) improve the banking sector-s ability to absorb shocks arising from financial and economic stress, whatever the source, (2) improve risk management and governance, (3) strengthen banks- transparency and disclosures. Similarly the reform target; (1) bank level or micro-prudential, regulation, which will help raise the resilience of individual banking institutions to periods of stress. (2) Macro-prudential regulations, system wide risk that can build up across the banking sector as well as the pro-cyclical implication of these risks over time. These two approaches to supervision are complementary as greater resilience at the individual bank level reduces the risk system wide shocks. Macroeconomic impact of Basel III; OECD estimates that the medium-term impact of Basel III implementation on GDP growth is in the range -0,05 percent to -0,15 percent per year. On the other hand economic output is mainly affected by an increase in bank lending spreads as banks pass a rise in banking funding costs, due to higher capital requirements, to their customers. Consequently the estimated effects on GDP growth assume no active response from monetary policy. Basel III impact on economic output could be offset by a reduction (or delayed increase) in monetary policy rates by about 30 to 80 basis points. The aim of this paper is to create a framework based on the recent regulations in order to prevent financial crises. Thus the need to overcome the global financial crisis will contribute to financial crises that may occur in the future periods. In the first part of the paper, the effects of the global crisis on the banking system examine the concept of financial regulations. In the second part; especially in the financial regulations and Basel III are analyzed. The last section in this paper explored the possible consequences of the macroeconomic impacts of Basel III.

Generalization of SGIP Surface Tension Force Model in Three-Dimensional Flows and Compare to Other Models in Interfacial Flows

In this paper, the two-dimensional stagger grid interface pressure (SGIP) model has been generalized and presented into three-dimensional form. For this purpose, various models of surface tension force for interfacial flows have been investigated and compared with each other. The VOF method has been used for tracking the interface. To show the ability of the SGIP model for three-dimensional flows in comparison with other models, pressure contours, maximum spurious velocities, norm spurious flow velocities and pressure jump error for motionless drop of liquid and bubble of gas are calculated using different models. It has been pointed out that SGIP model in comparison with the CSF, CSS and PCIL models produces the least maximum and norm spurious velocities. Additionally, the new model produces more accurate results in calculating the pressure jumps across the interface for motionless drop of liquid and bubble of gas which is generated in surface tension force.

Towards Better Understanding of the Concept of Tacit Knowledge – A Cognitive Approach

Tacit knowledge has been one of the most discussed and contradictory concepts in the field of knowledge management since the mid 1990s. The concept is used relatively vaguely to refer to any type of information that is difficult to articulate, which has led to discussions about the original meaning of the concept (adopted from Polanyi-s philosophy) and the nature of tacit knowing. It is proposed that the subject should be approached from the perspective of cognitive science in order to connect tacit knowledge to empirically studied cognitive phenomena. Some of the most important examples of tacit knowing presented by Polanyi are analyzed in order to trace the cognitive mechanisms of tacit knowing and to promote better understanding of the nature of tacit knowledge. The cognitive approach to Polanyi-s theory reveals that the tacit/explicit typology of knowledge often presented in the knowledge management literature is not only artificial but totally opposite approach compared to Polanyi-s thinking.

Combating Money Laundering in the Banking Industry: Malaysian Experience

Money laundering has been described by many as the lifeblood of crime and is a major threat to the economic and social well-being of societies. It has been recognized that the banking system has long been the central element of money laundering. This is in part due to the complexity and confidentiality of the banking system itself. It is generally accepted that effective anti-money laundering (AML) measures adopted by banks will make it tougher for criminals to get their "dirty money" into the financial system. In fact, for law enforcement agencies, banks are considered to be an important source of valuable information for the detection of money laundering. However, from the banks- perspective, the main reason for their existence is to make as much profits as possible. Hence their cultural and commercial interests are totally distinct from that of the law enforcement authorities. Undoubtedly, AML laws create a major dilemma for banks as they produce a significant shift in the way banks interact with their customers. Furthermore, the implementation of the laws not only creates significant compliance problems for banks, but also has the potential to adversely affect the operations of banks. As such, it is legitimate to ask whether these laws are effective in preventing money launderers from using banks, or whether they simply put an unreasonable burden on banks and their customers. This paper attempts to address these issues and analyze them against the background of the Malaysian AML laws. It must be said that effective coordination between AML regulator and the banking industry is vital to minimize problems faced by the banks and thereby to ensure effective implementation of the laws in combating money laundering.

Simulation of Dynamics of a Permanent Magnet Linear Actuator

Comparison of two approaches for the simulation of the dynamic behaviour of a permanent magnet linear actuator is presented. These are full coupled model, where the electromagnetic field, electric circuit and mechanical motion problems are solved simultaneously, and decoupled model, where first a set of static magnetic filed analysis is carried out and then the electric circuit and mechanical motion equations are solved employing bi-cubic spline approximations of the field analysis results. The results show that the proposed decoupled model is of satisfactory accuracy and gives more flexibility when the actuator response is required to be estimated for different external conditions, e.g. external circuit parameters or mechanical loads.

Principal Component Analysis using Singular Value Decomposition of Microarray Data

A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented via a singular value decomposition(SVD), is useful for analysis of microarray data. For application of PCA using SVD we use the DNA microarray data for the small round blue cell tumors(SRBCT) of childhood by Khan et al.(2001). To decide the number of components which account for sufficient amount of information we draw scree plot. Biplot, a graphic display associated with PCA, reveals important features that exhibit relationship between variables and also the relationship of variables with observations.

The Analysis of Printing Quality of Offset - Printing Ink with Coconut Oil Base

The objectives of this research are to produce prototype coconut oil based solvent offset printing inks and to analyze a basic quality of printing work derived from coconut oil based solvent offset printing inks, by mean of bringing coconut oil for producing varnish and bringing such varnish to produce black offset printing inks. Then, analysis of qualities i.e. CIELAB value, density value, and dot gain value of printing work from coconut oil based solvent offset printing inks which printed on gloss-coated woodfree paper weighs 130 grams were done. The research result of coconut oil based solvent offset printing inks indicated that the suitable varnish formulation is using 51% of coconut oil, 36% of phenolic resin, and 14% of solvent oil 14%, while the result of producing black offset ink displayed that the suitable formula of printing ink is using varnish mixed with 20% of coconut oil, and the analyzing printing work of coconut oil based solvent offset printing inks which printed on paper, the results were as follows: CIELAB value of black offset printing ink is at L* = 31.90, a* = 0.27, and b* = 1.86, density value is at 1.27 and dot gain value was high at mid tone area of image area.

Analysis of Feature Space for a 2d/3d Vision based Emotion Recognition Method

In modern human computer interaction systems (HCI), emotion recognition is becoming an imperative characteristic. The quest for effective and reliable emotion recognition in HCI has resulted in a need for better face detection, feature extraction and classification. In this paper we present results of feature space analysis after briefly explaining our fully automatic vision based emotion recognition method. We demonstrate the compactness of the feature space and show how the 2d/3d based method achieves superior features for the purpose of emotion classification. Also it is exposed that through feature normalization a widely person independent feature space is created. As a consequence, the classifier architecture has only a minor influence on the classification result. This is particularly elucidated with the help of confusion matrices. For this purpose advanced classification algorithms, such as Support Vector Machines and Artificial Neural Networks are employed, as well as the simple k- Nearest Neighbor classifier.

An FPGA Implementation of Intelligent Visual Based Fall Detection

Falling has been one of the major concerns and threats to the independence of the elderly in their daily lives. With the worldwide significant growth of the aging population, it is essential to have a promising solution of fall detection which is able to operate at high accuracy in real-time and supports large scale implementation using multiple cameras. Field Programmable Gate Array (FPGA) is a highly promising tool to be used as a hardware accelerator in many emerging embedded vision based system. Thus, it is the main objective of this paper to present an FPGA-based solution of visual based fall detection to meet stringent real-time requirements with high accuracy. The hardware architecture of visual based fall detection which utilizes the pixel locality to reduce memory accesses is proposed. By exploiting the parallel and pipeline architecture of FPGA, our hardware implementation of visual based fall detection using FGPA is able to achieve a performance of 60fps for a series of video analytical functions at VGA resolutions (640x480). The results of this work show that FPGA has great potentials and impacts in enabling large scale vision system in the future healthcare industry due to its flexibility and scalability.

The Investigations of Water-ethanol Mixture by Monte Carlo Method

Energetic and structural results for ethanol-water mixtures as a function of the mole fraction were calculated using Monte Carlo methodology. Energy partitioning results obtained for equimolar water-ethanol mixture and ether organic liquids are compared. It has been shown that at xet=0.22 the RDFs for waterethanol and ethanol-ethanol interactions indicated strong hydrophobic interactions between ethanol molecules and the local structure of solution is less structured at this concentration as at ether ones. Results obtained for ethanol-water mixture as a function of concentration are in good agreement with the experimental data.

Therapeutic Product Preparation Bioprocess Modeling

An immunomodulator bioproduct is prepared in a batch bioprocess with a modified bacterium Pseudomonas aeruginosa. The bioprocess is performed in 100 L Bioengineering bioreactor with 42 L cultivation medium made of peptone, meat extract and sodium chloride. The optimal bioprocess parameters were determined: temperature – 37 0C, agitation speed - 300 rpm, aeration rate – 40 L/min, pressure – 0.5 bar, Dow Corning Antifoam M-max. 4 % of the medium volume, duration - 6 hours. This kind of bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying. The aim of the paper is to present (by comparison) different models based on experimental data. The analysis criteria were modeling error and convergence rate. The estimated values and the modeling analysis were done by using the Table Curve 2D. The preliminary conclusions indicate Andrews-s model with a maximum specific growth rate of the bacterium in the range of 0.8 h-1.

Housing Defect of Newly Completed House: An Analysis Using Condition Survey Protocol (CSP) 1 Matrix

Housing is a basic human right. The provision of new house shall be free from any defects, even for the defects that people do normally considered as 'cosmetic defects'. This paper studies about the building defects of newly completed house of 72 unit of double-storey terraced located in Bangi, Selangor. The building survey implemented using protocol 1 (visual inspection). As for new house, the survey work is very stringent in determining the defects condition and priority. Survey and reporting procedure is carried out based on CSP1 Matrix that involved scoring system, photographs and plan tagging. The analysis is done using Statistical Package for Social Sciences (SPSS). The finding reveals that there are 2119 defects recorded in 72 terraced houses. The cumulative score obtained was 27644 while the overall rating is 13.05. These results indicate that the construction quality of the newly terraced houses is low and not up to an acceptable standard as the new house should be.

NonStationary CMA for Decision Feedback Equalization of Markovian Time Varying Channels

In this paper, we propose a modified version of the Constant Modulus Algorithm (CMA) tailored for blind Decision Feedback Equalizer (DFE) of first order Markovian time varying channels. The proposed NonStationary CMA (NSCMA) is designed so that it explicitly takes into account the Markovian structure of the channel nonstationarity. Hence, unlike the classical CMA, the NSCMA is not blind with respect to the channel time variations. This greatly helps the equalizer in the case of realistic channels, and avoids frequent transmissions of training sequences. This paper develops a theoretical analysis of the steady state performance of the CMA and the NSCMA for DFEs within a time varying context. Therefore, approximate expressions of the mean square errors are derived. We prove that in the steady state, the NSCMA exhibits better performance than the classical CMA. These new results are confirmed by simulation. Through an experimental study, we demonstrate that the Bit Error Rate (BER) is reduced by the NSCMA-DFE, and the improvement of the BER achieved by the NSCMA-DFE is as significant as the channel time variations are severe.

Temperature-dependent Structural Perturbation of Tuna Myoglobin

To unveil the mechanism of fast autooxidation of fish myoglobins, the effect of temperature on the structural change of tuna myoglobin was investigated. Purified myoglobin was subjected to preincubation at 5, 20, 50 and 40oC. Overall helical structural decay through thermal treatment up to 95oC was monitored by circular dichroism spectrometry, while the structural changes around the heme pocket was measured by ultraviolet/visible absorption spectrophotometry. As a result, no essential structural change of myoglobin was observed under 30oC, roughly equivalent to their body temperature, but the structure was clearly damaged at 40oC. The Soret band absorption hardly differed irrespective of preincubation temperature, suggesting that the structure around the heme pocket was not perturbed even after thermal treatment.

Neural Network Optimal Power Flow(NN-OPF) based on IPSO with Developed Load Cluster Method

An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Medical Image Segmentation Using Deformable Model and Local Fitting Binary: Thoracic Aorta

This paper presents an application of level sets for the segmentation of abdominal and thoracic aortic aneurysms in CTA datasets. An important challenge in reliably detecting aortic is the need to overcome problems associated with intensity inhomogeneities. Level sets are part of an important class of methods that utilize partial differential equations (PDEs) and have been extensively applied in image segmentation. A kernel function in the level set formulation aids the suppression of noise in the extracted regions of interest and then guides the motion of the evolving contour for the detection of weak boundaries. The speed of curve evolution has been significantly improved with a resulting decrease in segmentation time compared with previous implementations of level sets, and are shown to be more effective than other approaches in coping with intensity inhomogeneities. We have applied the Courant Friedrichs Levy (CFL) condition as stability criterion for our algorithm.

An Adaptive Memetic Algorithm With Dynamic Population Management for Designing HIV Multidrug Therapies

In this paper, a mathematical model of human immunodeficiency virus (HIV) is utilized and an optimization problem is proposed, with the final goal of implementing an optimal 900-day structured treatment interruption (STI) protocol. Two type of commonly used drugs in highly active antiretroviral therapy (HAART), reverse transcriptase inhibitors (RTI) and protease inhibitors (PI), are considered. In order to solving the proposed optimization problem an adaptive memetic algorithm with population management (AMAPM) is proposed. The AMAPM uses a distance measure to control the diversity of population in genotype space and thus preventing the stagnation and premature convergence. Moreover, the AMAPM uses diversity parameter in phenotype space to dynamically set the population size and the number of crossovers during the search process. Three crossover operators diversify the population, simultaneously. The progresses of crossover operators are utilized to set the number of each crossover per generation. In order to escaping the local optima and introducing the new search directions toward the global optima, two local searchers assist the evolutionary process. In contrast to traditional memetic algorithms, the activation of these local searchers is not random and depends on both the diversity parameters in genotype space and phenotype space. The capability of AMAPM in finding optimal solutions compared with three popular metaheurestics is introduced.