Search results for: risk models

Authors: Chih-Wei Lin, Duen-Ren Liu, Hui-Fang Chen

Abstract:

Effective knowledge support relies on providing operation-relevant knowledge to workers promptly and accurately. A knowledge flow represents an individual-s or a group-s knowledge-needs and referencing behavior of codified knowledge during operation performance. The flow has been utilized to facilitate organizational knowledge support by illustrating workers- knowledge-needs systematically and precisely. However, conventional knowledge-flow models cannot work well in cooperative teams, which team members usually have diverse knowledge-needs in terms of roles. The reason is that those models only provide one single view to all participants and do not reflect individual knowledge-needs in flows. Hence, we propose a role-based knowledge-flow view model in this work. The model builds knowledge-flow views (or virtual knowledge flows) by creating appropriate virtual knowledge nodes and generalizing knowledge concepts to required concept levels. The customized views could represent individual role-s knowledge-needs in teamwork context. The novel model indicates knowledge-needs in condensed representation from a roles perspective and enhances the efficiency of cooperative knowledge support in organizations.

Keywords: cooperative knowledge support, knowledge flow, knowledge-flow view, role-based models

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Authors: Maumita Bhattacharya

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Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, many real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build meta models or use an approximation of the actual fitness functions to be evaluated. These meta models are order of magnitude cheaper to evaluate compared to the actual function evaluation. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present two evolutionary algorithm frameworks which involve use of meta models for fitness function evaluation. The first framework, namely the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the metamodel are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The second model, DAFHEA-II, an enhanced version of the original DAFHEA framework, incorporates a multiple-model based learning approach for the support vector machine approximator to handle noisy functions [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions demonstrate their efficiency

Keywords: Meta model, Evolutionary algorithm, Stochastictechnique, Fitness function, Optimization, Support vector machine.

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Authors: Eakarat Boonreang

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The past two decades, Thailand faced the natural disasters, for instance, Gay typhoon in 1989, tsunami in 2004, and huge flood in 2011. The disaster management in Thailand was improved both structure and mechanism for cope with the natural disaster since 2007. However, the natural disaster management in Thailand has various problems, for examples, cooperation between related an organizations have not unity, inadequate resources, the natural disaster management of public sectors not proactive, people has not awareness the risk of the natural disaster, and communities did not participate in the natural disaster management. Objective of this study is to find the methods for capacity building in the natural disaster management of Thailand. The concept and information about the capacity building and the natural disaster management of Thailand were reviewed and analyzed by classifying and organizing data. The result found that the methods for capacity building in the natural disaster management of Thailand should be consist of 1) link operation and information in the natural disaster management between nation, province, local and community levels, 2) enhance competency and resources of public sectors which relate to the natural disaster management, 3) establish proactive natural disaster management both planning and implementation, 4) decentralize the natural disaster management to local government organizations, 5) construct public awareness in the natural disaster management to community, 6) support Community Based Disaster Risk Management (CBDRM) seriously, and 7) emphasis on participation in the natural disaster management of all stakeholders.

Keywords: Capacity Building, Community Based Disaster Risk Management, Natural Disaster Management, Thailand.

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Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

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In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: GPS based household surveys, transportation infrastructure, origin-destination trip matrices, traffic forecasts, transportation demand modeling, travel behavior patterns.

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Authors: R. Senthilkumar

Abstract:

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.

Keywords: BiShrink, Image-Denoising, PSNR, Shrinkage function

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Authors: G. C. Mahata, A. Goswami

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This paper deals with infinite time horizon fuzzy Economic Order Quantity (EOQ) models for deteriorating items with  stock dependent demand rate and nonlinear holding costs by taking deterioration rate θ0 as a triangular fuzzy number  (θ0 −δ 1, θ0, θ0 +δ 2), where 1 2 0 0 <δ ,δ <θ are fixed real numbers. The traditional parameters such as unit cost and ordering  cost have been kept constant but holding cost is considered to vary. Two possibilities of variations in the holding cost function namely, a non-linear function of the length of time for which the item is held in stock and a non-linear function of the amount of on-hand inventory have been used in the models. The approximate optimal solution for the fuzzy cost functions in both these cases have been obtained and the effect of non-linearity in holding costs is studied with the help of a numerical example.

Keywords: Inventory Model, Deterioration, Holding Cost, Fuzzy Total Cost, Extension Principle.

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Authors: S. K. Mohan, T. Viruthagiri, C. Arunkumar

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Tannase (tannin acyl hydrolase, E.C.3.1.1.20) is an important hydrolysable enzyme with innumerable applications and industrial potential. In the present study, a kinetic model has been developed for the batch fermentation used for the production of tannase by A.flavus MTCC 3783. Maximum tannase activity of 143.30 U/ml was obtained at 96 hours under optimum operating conditions at 35oC, an initial pH of 5.5 and with an inducer tannic acid concentration of 3% (w/v) for a fermentation period of 120 hours. The biomass concentration reaches a maximum of 6.62 g/l at 96 hours and further there was no increase in biomass concentration till the end of the fermentation. Various unstructured kinetic models were analyzed to simulate the experimental values of microbial growth, tannase activity and substrate concentration. The Logistic model for microbial growth , Luedeking - Piret model for production of tannase and Substrate utilization kinetic model for utilization of substrate were capable of predicting the fermentation profile with high coefficient of determination (R2) values of 0.980, 0.942 and 0.983 respectively. The results indicated that the unstructured models were able to describe the fermentation kinetics more effectively.

Keywords: Aspergillus flavus, Batch fermentation, Kinetic model, Tannase, Unstructured models.

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Authors: M. H. Pol, A. Bidi, A. V. Hoseini

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Estimation of natural frequency of structures is very important and isn-t usually calculated simply and sometimes complicated. Lack of knowledge about that caused hard damage and hazardous effects. In this paper, with using from two different models in FEM method and based on hydrodynamic mass of fluids, natural frequency of an especial bearing (Fig. 1) in an electric field (or, a periodic force) is calculated in different stiffness and different geometric. In final, the results of two models and analytical solution are compared.

Keywords: Natural frequency of the bearing, Hydrodynamic mass of fluid method.

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Authors: M. A. Omoloye, M. I. Yusuff, O. K. S. Emiola

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The use of mathematical models for solving biological problems varies from simple to complex analyses, depending on the nature of the research problems and applicability of the models. The method is more common nowadays. Many complex models become impractical when transmitted analytically. However, alternative approach such as numerical method can be employed. It appropriateness in solving linear and non-linear model equation in Differential Transformation Method (DTM) which depends on Taylor series make it applicable. Hence this study investigates the application of DTM to solve dynamic transmission of Lassa fever model in a population. The mathematical model was formulated using first order differential equation. Firstly, existence and uniqueness of the solution was determined to establish that the model is mathematically well posed for the application of DTM. Numerically, simulations were conducted to compare the results obtained by DTM and that of fourth-order Runge-Kutta method. As shown, DTM is very effective in predicting the solution of epidemics of Lassa fever model.

Keywords: Differential Transform Method, Existence and uniqueness, Lassa fever, Runge-Kutta Method.

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Authors: Nabhan Hamadneh, Michael Dixon, Peter Cole, David Murray

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This work presents the Risk Threshold RED (RTRED) congestion control strategy for TCP networks. In addition to the maximum and minimum thresholds in existing RED-based strategies, we add a third dropping level. This new dropping level is the risk threshold which works with the actual and average queue sizes to detect the immediate congestion in gateways. Congestion reaction by RTRED is on time. The reaction to congestion is neither too early, to avoid unfair packet losses, nor too late to avoid packet dropping from time-outs. We compared our novel strategy with RED and ARED strategies for TCP congestion handling using a NS-2 simulation script. We found that the RTRED strategy outperformed RED and ARED.

Keywords: AQM, congestion control, RED, TCP.

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Authors: Fulya Aydın, Ayşe Kuleyin

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The purpose of this study is to investigate the capacity of natural Turkish zeolite for NH4-N removal from landfill leachate. The effects of modification and initial concentration on the removal of NH4-N from leachate were also investigated. The kinetics of adsorption of NH4-N has been discussed using three kinetic models, i.e., the pseudo-second order model, the Elovich equation, the intraparticle diffuion model. Kinetic parameters and correlation coefficients were determined. Equilibrium isotherms for the adsorption of NH4-N were analyzed by Langmuir, Freundlich and Tempkin isotherm models. Langmuir isotherm model was found to best represent the data for NH4-N.

Keywords: Leachate, Ammonium, zeolite

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Authors: Yiannis G. Smirlis

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The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models.

Keywords: Data envelopment analysis, interval DEA, efficiency classification, efficiency prediction.

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Authors: Reza Gharehkhani

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Sedimentation in reservoirs lowers the quality of consumed water, reduce the volume of reservoir, lowers the controllable amount of flood, increases the risk of water overflow during possible floods and the risk of reversal and reduction of dam's useful life. So in all stages of dam establishment such as cognitive studies, phase-1 studies of design, control, construction and maintenance, the problem of sedimentation in reservoir should be considered. What engineers need to do is examine and develop the methods to keep effective capacity of a reservoir, however engineers should also consider the influences of the methods on the flood disaster, functions of water use facilities and environmental issues.This article first examines the sedimentation in reservoirs and shows how to control it and then discusses the studies about the sedimens in Siazakh Dam.

Keywords: Sedimentation, Reservoir, Sediment Control, Dam

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Authors: T. K. Sriram, Joydip Dhar

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Models based on stage structure have found varied applications in population models. This paper proposes a stage structured model to study the trends in the computer and video game playing population of US. The game paying population is divided into three compartments based on their age group. After simulating the mathematical model, a forecast of the number of game players in each stage as well as an approximation of the average age of game players in future has been made.

Keywords: Age structure, Forecasting, Mathematical modeling, Stage structure.

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Authors: Teh Raihana Nazirah Roslan, Siti Zulaiha Ibrahim, Sharmila Karim

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A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study.

Keywords: Cox-Ingersoll-Ross model, equity warrants, Heston model, hybrid models, stochastic.

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Authors: S. Cagin, X. Fischer, E. Delacourt, N. Bourabaa, C. Morin, D. Coutellier, B. Carré, S. Loumé

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The complexity of scavenging by ports and its impact on engine efficiency create the need to understand and to model it as realistically as possible. However, there are few empirical scavenging models and these are highly specialized. In a design optimization process, they appear very restricted and their field of use is limited. This paper presents a comparison of two methods to establish and reduce a model of the scavenging process in 2-stroke diesel engines. To solve the lack of scavenging models, a CFD model has been developed and is used as the referent case. However, its large size requires a reduction. Two techniques have been tested depending on their fields of application: The NTF method and neural networks. They both appear highly appropriate drastically reducing the model’s size (over 90% reduction) with a low relative error rate (under 10%). Furthermore, each method produces a reduced model which can be used in distinct specialized fields of application: the distribution of a quantity (mass fraction for example) in the cylinder at each time step (pseudo-dynamic model) or the qualification of scavenging at the end of the process (pseudo-static model).

Keywords: Diesel engine, Design optimization, Model reduction, Neural network, NTF algorithm, Scavenging.

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Authors: Jaime R. S. Fonseca

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Nevertheless the widespread application of finite mixture models in segmentation, finite mixture model selection is still an important issue. In fact, the selection of an adequate number of segments is a key issue in deriving latent segments structures and it is desirable that the selection criteria used for this end are effective. In order to select among several information criteria, which may support the selection of the correct number of segments we conduct a simulation study. In particular, this study is intended to determine which information criteria are more appropriate for mixture model selection when considering data sets with only categorical segmentation base variables. The generation of mixtures of multinomial data supports the proposed analysis. As a result, we establish a relationship between the level of measurement of segmentation variables and some (eleven) information criteria-s performance. The criterion AIC3 shows better performance (it indicates the correct number of the simulated segments- structure more often) when referring to mixtures of multinomial segmentation base variables.

Keywords: Quantitative Methods, Multivariate Data Analysis, Clustering, Finite Mixture Models, Information Theoretical Criteria, Simulation experiments.

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Authors: Julinda Keçi

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In many countries, governments have been promoting the involvement of private sector entities to enter into long-term agreements for the development and delivery of large infrastructure projects, with a focus on overcoming the limitations upon public fund of the traditional approach. The involvement of private sector through public private partnerships (PPP) brings in new capital investments, value for money and additional risks to handle. Worldwide research studies have shown that an objective, systematic, reliable and useroriented risk assessment process and an optimal allocation mechanism among different stakeholders is crucial to the successful completion. In this framework, this paper, which is the first stage of a research study, aims to identify the main risks for the delivery of PPP projects. A review of cross-countries research projects and case studies was performed to map the key risks affecting PPP infrastructure delivery. The matrix of mapping offers a summary of the frequency of factors, clustered in eleven categories: construction, design, economic, legal, market, natural, operation, political, project finance, project selection and relationship. Results will highlight the most critical risk factors, and will hopefully assist the project managers in directing the managerial attention in the further stages of risk allocation.

Keywords: Construction, infrastructure, public private partnerships, risks.

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Authors: Philip Feig, Klaus Gschwendtner, Julian Schatz, Frank Diermeyer

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Projects of accident research analysis are mostly focused on accidents involving personal damage. Property damage only has a high frequency of occurrence combined with high economic impact. This paper describes main influencing parameters for the extent of damage and presents a repair cost model. For a prospective evaluation method of the monetary effect of advanced driver assistance systems (ADAS), it is necessary to be aware of and quantify all influencing parameters. Furthermore, this method allows the evaluation of vehicle concepts in combination with an ADAS at an early point in time of the product development process. In combination with a property damage database and the introduced repair cost model relevant test scenarios for specific vehicle configurations and their individual property damage risk may be determined. Currently, equipment rates of ADAS are low and a purchase incentive for customers would be beneficial. The next ADAS generation will prevent property damage to a large extent or at least reduce damage severity. Both effects may be a purchasing incentive for the customer and furthermore contribute to increased traffic safety.

Keywords: Property damage analysis, effectiveness, ADAS, damage risk, accident research, accident scenarios.

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Authors: B.A.Gobin, R.K.Subramanian

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This paper gives an overview of the mapping mechanism of SEAM-a methodology for the automatic generation of knowledge models and its mapping onto Java codes. It discusses the rules that will be used to map the different components in the knowledge model automatically onto Java classes, properties and methods. The aim of developing this mechanism is to help in the creation of a prototype which will be used to validate the knowledge model which has been generated automatically. It will also help to link the modeling phase with the implementation phase as existing knowledge engineering methodologies do not provide for proper guidelines for the transition from the knowledge modeling phase to development phase. This will decrease the development overheads associated to the development of Knowledge Based Systems.

Keywords: KBS, OWL, ontology, knowledge models

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Authors: Tanvir Ahmed, Thanh N. Long, Phan T. Huong, Donald E. Stewart

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This paper presents views on condom use and the contexts of safe and unsafe sexual practices with different sexual partners and their relationships among Injecting Drug Users (IDUs) in Hai Phong, Vietnam. Fifteen IDUs participated and two local interviewers conducted qualitative semi-structured face-to-face interviews in September-October, 2012 in Vietnamese language. Data were analyzed thematically. Non-protective condom attitudes include negotiate or convince Female Sex Workers (FSW); not realizing risk, importance or necessity; partner doesn’t like, and having extra money/drug from clients. On the other hand, self-awareness, family-consciousness, suspicion of STI presence, fear of getting HIV, and client negotiation sometimes resulted in a safe-sex practice. A thematic diagram was developed to present the relationship (strong/weak) between condom attitude and sexual practice (safe/unsafe) by partner types. The experiences and views reflected in the qualitative information emphasize the heightened need for safe-sex education especially among young IDUs (male/female) highlighting sexual transmission risk.

Keywords: AIDS, HIV, injecting drug user, risk behaviors, Vietnam.

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Authors: Hossam Shalabi, George Hadjisophocleous

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The Canadian Nuclear Power Plants (NPPs) use some portions of NUREG/CR-6850 in carrying out Fire Probabilistic Risk Assessment (PRA). An assessment for the applicability of NUREG/CR-6850 to CANDU reactors was performed and a CANDU Fire PRA was introduced. There are 19 operating CANDU reactors in Canada at five sites (Bruce A, Bruce B, Darlington, Pickering and Point Lepreau). A fire load density survey was done for all Fire Safe Shutdown Analysis (FSSA) fire zones in all CANDU sites in Canada. National Fire Protection Association (NFPA) Standard 557 proposes that a fire load survey must be conducted by either the weighing method or the inventory method or a combination of both. The combination method results in the most accurate values for fire loads. An updated CANDU Fire PRA model is demonstrated in this paper that includes the fuel survey in all Canadian CANDU stations. A qualitative screening step for the CANDU fire PRA is illustrated in this paper to include any fire events that can damage any part of the emergency power supply in addition to FSSA cables.

Keywords: Fire safety, CANDU, nuclear, fuel densities, FDS, qualitative analysis, fire probabilistic risk assessment.

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Authors: Zhaoan Wang

Abstract:

Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: Automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation.

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Authors: Irisa Murniece, Daina Karklina, Ruta Galoburda

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From food consumption surveys has been found that potato consumption comparing to other European countries is one of the highest. Hence acrylamide (AA) intake coming from fried potatoes in population might be high as well. The aim of the research was to determine acrylamide content and estimate intake of acrylamide from roasted potatoes bred and cultivated in Latvia. Five common Latvian potato varieties were selected: Lenora, Brasla, Imanta, Zile, and Madara. A two-year research was conducted during two periods: just after harvesting and after six months of storage. Time and temperature (210 ± 5°C) was recorded during frying. AA was extracted from potatoes by solid phase extraction and AA content was determined by LC-MS/MS. estimated intake of acrylamide ranges from 0.012 to 0.496μgkg-1 BW per day.

Keywords: potato, roasting, variety, acrylamide, Latvia, risk assessment.

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Authors: Masoud Sadeghian, Alireza Fatehi

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In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.

Keywords: Cement Rotary Kiln, Fault Detection, Delay Estimation Method, Locally Linear Neuro Fuzzy Model, LOLIMOT.

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Authors: Mamoun Sqali, Mohamed Wassim Trojet

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The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.

Keywords: Scenarios, DEVS, synthesis, validation and verification, simulation, formal verification, z notation.

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Authors: F. Z. Eddoug, J. Benhra, R. Benabbou

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Excellence and Success are the ultimate goal for any project and in order to achieve it, every project manager looks for the convenient tools and methods. This work proposes a framework that seeks an efficient management of general project through a lean and agile approach. In order to get this objective, the article was divided in two stages, the first one was emphasized on exploring and analyzing the existing project management models and then in the second one the desired framework was created, beginning by focusing on seven existing models and then proposing for each phase of the framework the convenient lean and agile tools.

Keywords: Agility, hybrid project management, lean, scrum.

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Authors: H. Zarei, M. H. Fazel Zarandi, M. Karbasian

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A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1st phase); Then, the estimated values are aggregated with the investor preferences (2nd phase) to produce convenient stock portfolio. In the 1st phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-Sugeno- Kang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN" rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2nd phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering.

Keywords: Stock Portfolio Selection, Fuzzy Rule-Base ExpertSystems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

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Authors: V. Thulasi Bai, Srivatsa S. K.

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Deaths from cardiovascular diseases have decreased substantially over the past two decades, largely as a result of advances in acute care and cardiac surgery. These developments have produced a growing population of patients who have survived a myocardial infarction. These patients need to be continuously monitored so that the initiation of treatment can be given within the crucial golden hour. The available conventional methods of monitoring mostly perform offline analysis and restrict the mobility of these patients within a hospital or room. Hence the aim of this paper is to design a Portable Cardiac Telemedicine System to aid the patients to regain their independence and return to an active work schedule, there by improving the psychological well being. The portable telemedicine system consists of a Wearable ECG Transmitter (WET) and a slightly modified mobile phone, which has an inbuilt ECG analyzer. The WET is placed on the body of the patient that continuously acquires the ECG signals from the high-risk cardiac patients who can move around anywhere. This WET transmits the ECG to the patient-s Bluetooth enabled mobile phone using blue tooth technology. The ECG analyzer inbuilt in the mobile phone continuously analyzes the heartbeats derived from the received ECG signals. In case of any panic condition, the mobile phone alerts the patients care taker by an SMS and initiates the transmission of a sample ECG signal to the doctor, via the mobile network.

Keywords: WET, ECG analyzer, Bluetooth, mobilecellular network, high risk cardiac patients.

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Authors: M.N. Khan, T. Rehman, Z Iqbal, M.S Sajid, M Ahmad, M Riaz

Abstract:

A cross-sectional study was carried out to determine the prevalence, species characterization and associated risk factors with Eimeria (E.) in sheep of district Toba Tek Singh from April, 2009 to March, 2010. Of the total 486 faecal samples examined for Eimeria, 209 (43%) were found infected with five species of Eimeria. Amongst the identified species of Eimeria, E. ovinoidalis was the commonest one (48.32%), followed in order by E. ahsata, E. intricata, E. parva and E. faurei with prevalence of 45.45, 28.71, 24.40 and 19.14 percent respectively. Peak prevalence was observed in August. Wet season (rainy and post-rainy) was found to be favourable for Eimeria infection. Lambs had significantly higher prevalence (P < 0.05) of Eimeria than adults. Similarly higher prevalence of Eimeria was observed in female as compared to male. Among management and husbandry practices; watering system, housing system, floor type and herd size strongly influenced the prevalence of Eimeria. Coccidiosis was more prevalent in closed housing system, non-cemented floor type, pond watered animals and larger herds (P < 0.05) as compared to open housing system, partially cemented floor type, tap watered animals and smaller herds respectively. Feeding system, breed and body condition of animals were not found as risk factors (P>0.05) influencing prevalence of Eimeria.

Keywords: Eimeria, Pakistan prevalence, sheep.

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