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Abstract:

Pulsatile flow of blood in an elastic circular tube with slip at the permeable walls is investigated in the present analysis solutions for axial and radial velocity has constructed. The volume tri crate of blood flow also measured in the axial direction. The expression for flow characteristic, velocity profile are obtained. Numerical results are shown in tabular form. The effect of slip velocity, size of the artery, viscosity on the flow are shown graphically and discussed briefly.

Keywords:

Refference:

I.Chandran K.B. Cardiovascular Biomechanics , New YorkUniversity ,1992

II.Kathleen Wilkie Human blood flow2003

III.An Introduction to Mathematical Physiology & Biology,Cambridge University Press,P.J. MAJUMDER1999

IV.Mathematical Biology J . D . Murray Springer 3rd. EditionP.1471532004

V.MHD Steady flow in a channel .With slip at the permeableBoundariesOld Makinde , E. OsalusiRom. Journ.Phys. Vol 51P319-328, Bucharest2006

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Abstract:

Bangladesh bureau of statistics (BBS) publish a statistical year book in every year where comprehensive and systematic summary of basic statistical information of Bangladesh covering wide range of fields. BBS also forecast different sectors such aseconomics, weather, agriculture etc in different time in this country. In this paper wemainly concern on the wheat, rice and maize foodgrain which plays a vital role ineconomic development of Bangladesh. The main purposes of this paper as to comparewhich techniques are better BBS’s or statistical techniques for forecasting. There aredifferent forecasting models are available in statistics among these we used Auto regressive (AR), Moving Average(MA), Autoregressive Moving Average (ARMA)and Auto regressive Integrated Moving Average (ARIMA) models. For this reason, weclarify the stationary and non-stationary series by graphical method. On the basis of that,the stationary model is being set up asthe forecasting purpose. After analyze, we compare the forecasting result of our selective foodgrain and find that forecasted valuesusing statistical techniques are nearest to the actual values compare to BBS’s project edvalues.

Keywords:

ARIMA,ARMA,Forecast,Foodgrain,

Refference:

I. Abdullah, L. (2012),ARIMA Model for Gold Bullion Coin Selling Prices Forecasting,International Journal of Advances in Applied Sciences. Vol. 1, No.4, pp. 153-158.

II. Anderson, T.W. (1984),An Introduction to Multivariate Statistical Analysis, 2nded.New York:John Wiley and Sons Inc.

III. Arumugan, P. and Anithakumari, V. (2013),Fuzzy Time Series Method for Forecasting TaiwanExport Data,International Journal of Engineering trendsand Technology. Vol.8,pp. 3342-3347.

IV. Box, G. E. P. and Jenkins, G. M. (1976),Time Series Analysis: Forecasting and Control,San Francisco: Holden-Day.

V. Brokwell, P.J. and Davis, R.A. (1997),Introduction to Time Series and Forecasting, Springer,New York.

VI.Clements, M. and Hendry, D. (1998),Forecasting Economic Time Series,United UniversityPress, Cambridge.

VII. Deepak, P., et al. (2015), A Comparison of forecasting methods: Fundamentals,Polling, Prediction Markets, and Experts,A Journal of Prediction Markets,Vol.23, No.2, pp.1-31.

VIII. Diebold, F. (2004),Elements of Forecasting, 3rded. Thomsos sourth-westrn,India.

IX. Ediger, S.A, (2006),ARIMAForecasting of Primary Energy Demand by Fuel inTurkey,Energypolicy, Vol. 35, pp.1-8.

X. Gouriroux, C. and Monfort, A. (1997),Time Series and Dynammic Models,Giampiero,M.Gallo Cambridge.

XI. Gujarati, D.N. (2004),Basic Econometrics, 4thed.,McGraw Hill, New York.

XII. Hannan, E.J. (1994),Multiple Time Series,New York: John Wiley & Sons Inc.

XIII. Kumar, et al. (2009),Surface flux modelingusing ARIMA technique in humansubtropicalmonsoon area,Journal of Atmospheric and Solar-TerrestrialPhysics. Vol. 71, pp. 1293-1298.

XIV. Lloret, et al. (2000),Time Series Modeling of Landings in NorthMediterranean Sea,ICESJournal of Marine Science: Journal du Conseil. Vol.57, pp. 171-184.

XV. Mitrea, C. A., Lee, C. K.M. and Wu,Z. (2009), A Comparison betweenNeural Networks and Traditional Forecasting Methods: A Case Study,International Journal of Engineering Business Management, Vol. 1, No. 2, pp.19-24.

XVI. Mucuk, M. and Uysal, D. (2009),Turkey’s Energy Demand,Current ResearchJournal of SocialSciences, Vol.1(3), pp. 123-128.

XVII. Pingfan, H. and Zhibo, T. (2014), A comparison study of the forec asingperformance of three international organizations,JEL codes: C30, C80.

XVIII. Prindyck,R.S. and Rubinfeld, D.L. (1981),Economic Models and EconomicForecasts,3rded.McGraw-Hill, Inc.

XIX. Slvanathan, E. A. (1991),A Note on the Accuracy of Business Economists GoldPrice Forecasts,Australian Journal of Management. Vol. 16, pp. 91-94

XX. Tseng, et al. (2001),Fuzzy ARIMA model for forecasting the foreign exchangemarket,FuzzySets and Systems. Vol. 118, pp. 1-11.

XXI. Wood, et al. (1996), Classifying Trend Movements in the MSCI U.S.A.Capitalmarket Index-A,Comparison of Regressions, ARIMA and Neural Network Method.Computers &Operation Research. Vol. 23, pp. 611-622.

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Abstract:

MHD boundary layer forced convection flow along a shrinking surface withvariable heat and mass flux in the presence of heat source is studied. The flow isproducedowing to linear shrinking of the sheet and is influenced by uniform transversemagnetic field. The boundary layer partial differential equations of momentum, heat andmass transfer equations are converted into nonlinear ordinary differential equations bysimilarity transformation. Numerical solution of the resulting boundary value problem isobtained using Nachtsheim-Swigert Shooting iteration scheme along with the sixth orderRunge-Kutta method. The effects of different parameter on velocity, temperature andconcentration are shown graphically. Skin friction coefficient, Nusselt number andSherwood number are also for different values of the parameter are also involved in thestudy.

Keywords:

,

Refference:

I.Chen, C.K. and Char, M.I (1988),“Heat transfer of a continuously stretchingsurface with suction and blowing”,Journal of Mathematical Analysis andApplications135[2], 568-580.

II.Ali, M.E. (1995), “Thermal boundary layer on a power-law stretched surfacewith suction or injection”,International Journal of Heat and Fluid Flow16[4], 280-290.

III.Elbashbeshy, “E.M.A. (1998).Heat transfer over a stretching surface withvariable surface heat flux”,Journal of Physics D: Applied Physics31 [16],1951-1954.

IV.Liao, S.J. (2005), “A new branch of solution of boundary layer flows over apermeable stretching plate”,International Journal of Heat and Mass Transfer48[12], 2529-2539.

V.Bhargava, R. Sharma, S. Takhar, H.S. and Bhargava, P (2007), “Numericalsolutions for Micropolar transport phenomena over nonlinear stretchingsheet”,Nonlinear Analysis: Modeling and Control12[1], 45-63.

VI.Khedr, M.E., Chamka, A.J. and Bayomi, M. (2009), “MHD flow of amicropolar fluid past a stretched permeable surface with heat generation orabsorption”,Nonlinear Analysis: Modeling and Control14[1], 27-40.

VII.S. P. Anjali Devi and J. W. S. Raj (2014), “Numerical Simulation of Magneto-hydrodynamic Forced Convective Boundary Layer Flow past aStretching/Shrinking Sheet Prescribed with Variable Heat Flux in thePresence of Heat Source and Constant Suction ”,Journal of Applied FluidMechanics,7[3], 415-423, 2014.

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Abstract:

A two dimensional problem of electrostriction with a hole in the of Pascal's limacon is solvcd by complwx variable method. The distributions of stresses in an infinite dielection plate when the hole is filled up by air is subjected to an electric filed unifrom at infinity as well as it is acted on by appiled two dimensional tractions at infinity. The hoop stress is calculated on the boundary of the hole.

Keywords:

,

Refference:

I. Stratton, J.D.: Electromagnetic Theory, Mc Graw- Hill, new York (1941).

II. Landau, L.D. Electrodynamics of continuous Media Addision Wesley (1960).

III. Knops, R.J. Quart Jour. Math. Vol.16(1963) p.377.

IV. Maikap G.H. and Sengupta, P.R. Acta Cien. Ind. Vol.17(1991) p.498.

 

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