Journal Vol – 17 No -2, February 2022

ANALYSIS OF RACETRACK RESONATOR USING SIGNAL PROCESSING TECHNIQUE

Authors:

Sabitabrata Dey,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00001

Abstract:

Optical double racetrack resonator (ODRR) and optical quadruple racetrack resonator (OQRR) made of Silicon-on-insulator (SOI) with their effective refractive indices changing with respect to frequency have been analyzed for obtaining optical filter with wider ranges of free spectral range (FSR). FSR expansion is based on the Vernier principle. Delay line signal processing in Z- domain and Mason’s gain formula is being used for analyzing these ODRR and OQRR. A free spectral range of 4.87THz is obtained for the drop port. Further, the change in the dimensions of the racetrack resonators produced an enhanced FSR of 5.77THz for ODRR. Combining both this model of ODRR we obtained an OQRR model that produces FSR as much as 6.86THz. Apart from obtaining wider FSR, this architecture exhibits interstitial spurious transmission of almost -50dB with negligible resonance loss. Group delay, dispersion characteristics, and finesse have also been determined for the architecture.

Keywords:

Racetrack resonator,Mason’s gain formula,free spectral range,Vernier principle,Resonance loss,Group delay,Dispersion,

Refference:

I. A. Wirth L, M.G da Silva, D.M.C Neves, A.S.B Sombra, “Nanophotonicgraphene-basedracetrack- resonatoradd/drop filter”, Optics Communications, 366(2016) 210-220, Elsevier.
II. A. Oppenheim, R. Schafer, “Digital Signal Processing”, 2nd edition Prentice-Hall, IncEnglewood, NJ, 1975.
III. Christi K.Madsen, Jian H Zhao, “Optical filter design and analysis, A signal processing approach”, John Wiley & sons, Inc, New York, 1999.
IV. D. Marcuse, “Bending losses of the asymmetric slab waveguide,” Bell System Tech. J. 50 (8), 2551–2563 (1971).
V. Fengnian Xia, Lidija Sekaric and Yurii A. Vlasov,” Mode conversion losses in silicon-on-insulator photonic wire based racetrack resonators”, 1 May 2006, Vol. 14, No. 9, Optics Express.3872.
VI. H. Liu, C. F. Lam, and C. Johnson, “Scaling Optical Interconnects in Data center Networks Opportunities and Challenges for WDM,” 2010 18th IEEE Symp. High Perf. Interconnects, pp. 113–16.
VII. Intel Silicon Innovation: Fueling New Solutions for the Digital Planet, www.intel.com/technology/silicon.
VIII. J. T. Robinson, L. Chen, and M. Lipson, “On-chip gas detection in silicon optical microcavities,” Opt. Express, vol. 16, pp. 4296–4301, March 2008.
IX. Landobasa Y.M. Tobing, Dumon Pieter, “Fundamental principles of operation and notes on fabrication of photonic microresonators, Photonic Microring Research and Application”, 156, Springer, 2010 chap-1.
X. Otto Schwelb, „Transmission, Group Delay, and Dispersion in Single-Ring Optical Resonators and Add/Drop Filters- A Tutorial Overview‟, IEEE journal of Lightwave technology 22 (5) (2004).
XI. P. W. Coteus J. U. Knickerbocker, C. H. Lam, Y. A. Vlasov, “Technologies for exascale systems”, IBM J. Res. & Dev. Vol. 55 No. 5 Paper 14 September/October 2011.
XII. R. Boeck, W. Shi, L. Chrostowski, N.A.F Jaeger, “FSR-Eliminated Vernier racetrack Resonators using Grating-Assisted Couplers”, IEEE Photonics journal, DOI: 10.1109/JPHOT.2013.2280342, IEEE.
XIII. Robi Boeck, Nicolas A. F. Jaeger, Nicolas Rouger, Lukas Chrostowski “Series-coupled silicon racetrack resonators and the Vernier effect: theory and measurement” Optics Express (2010). OCIS codes: (130.0130) Integrated optics; (130.7408) Wavelength filtering devices; (230.5750) Resonators.
XIV. Robi Boeck, Jonas Flueckiger, Nicolas Rouger, Lukas Chrostowski ” Experimental performance of DWDM quadruple Vernier racetrack resonators ” OSA (2013) OCIS codes (230.7408) Wavelength filtering devices; (230.5750) Resonators.
XV. S.J Mason, “Feedback Properties of Signal Flow Graphs,” Proc. IRE, Vol. 44, no. 7, pp. 920-926, July 1975.
XVI. S. Dey, S. Mandal, “Modeling and analysis of quadruple optical ring resonator performance as optical filter using Vernier principle”, Optics Communications 285 (2012) 439–446.
XVII. Sabitabrata Dey, S.Mandal, “ Wide free-spectral-range triple ring resonator as optical filter,” Optical Engineering, SPIE,Vol. 50(8),pp 084601-(1-9), August, 2011.
XVIII. Yurii A. Vlasov, “Silicon CMOS-Integrated Nano-Photonics for Computer and Data Communications Beyond 100G”, IEEE Communications Magazine, February 2012.

View Download

MINIMIZATION OF TORQUE RIPPLES IN A SWITCHED RELUCTANCE MACHINE BY AN OPTIMAL SWITCHING ANGLE WITHIN A LOW INDUCTANCE REGION

Authors:

Sadam Hussain Lashari,Ali Asghar Memon,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00002

Abstract:

     Because of its high starting torque and improved performance in a variety of operating situations, the switched reluctance machine (SRM) has emerged as a potential challenger in the family of electrical machines. SRM has been a new addition to the industrial market in recent years. Drawbacks of SRMs are the torque ripple and acoustic noise. This research focuses on the minimization of torque ripples in a Switched Reluctance Machine by optimal switching angle in a low inductance region for a range of speed. For this, simulation is performed with the aim that SRM operation in a low inductance region will take place with low torque ripples. The finding of this research will help in better performance of the machine when operated at the desired angle.

Keywords:

Experimental Validation,Switched Reluctance Machine,Static Torque,Torque Ripples,

Refference:

I. A. A. Memon (2012). Prediction of compound losses in a switched reluctance machine and inverter (Doctoral dissertation) University of Leeds (School of Electronic and Electrical Engineering)
II. Ghousia, Syeda Fatima. “Impact analysis of dwell angles on current shape and torque in switched reluctance motors.” International journal of power electronics and drive systems 2, no. 2 (2012): 160.
III. Xu, Y.Z., Zhong, R., Chen, L. and Lu, S.L., 2012. Analytical method to optimise turn-on angle and turn-off angle for switched reluctance motor drives. IET Electric Power Applications, 6(9), pp.593-603.
IV. Suryadevara, R. and Fernandes, B.G., 2013, December. Control techniques for torque ripple minimization in switched reluctance motor: An overview. In 2013 IEEE 8th International Conference on Industrial and Information Systems (pp. 24-29).
V. Nashed, M.N., Mahmoud, S.M., El-Sherif, M.Z. and Abdel-Aliem, E.S., 2014. Optimum change of switching angles on switched reluctance motor performance. International Journal of Current Engineering and Technology, 4(2).
VI. Wei, Ye, Ma Qishuang, Zhang Poming, and Guo Yangyang. “Torque ripple reduction in switched reluctance motor using a novel torque sharing function.” In 2016 IEEE International Conference on Aircraft Utility Systems (AUS), pp. 177-182. IEEE, 2016.
VII. Memon, Ali Asghar, Syed Asif Ali Shah, Wajiha Shah, Mazhar Hussain Baloch, Ghulam Sarwar Kaloi, and Nayyar Hussain Mirjat. “A Flexible Mathematical Model for Dissimilar Operating Modes of a Switched Reluctance Machine.” IEEE Access 6 (2018): 9643-9649.
VIII. Üstün, O. and Önder, M., 2020. An Improved Torque Sharing Function to Minimize Torque Ripple and Increase Average Torque for Switched Reluctance Motor Drives. Electric Power Components and Systems, 48 (6-7), pp.667-681.
IX. Keerthana, C. and Sundaram, M., 2020, June. State of Art of Control Techniques adopted for Torque Ripple Minimization in Switched Reluctance Motor Drives. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184) (pp. 105-110).
X. Touati, Z., Mahmoud, I. and Khedher, A., 2021, March. Torque Ripple Minimization Approach of a 3-phase Switched Reluctance Motor. In 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD) (pp. 533-538).
XI. Ren, P., Zhu, J., Jing, Z., Guo, Z. and Xu, A., 2021. Minimization of torque ripple in switched reluctance motor based on MPC and TSF. IEEJ Transactions on Electrical and Electronic Engineering, 16(11), pp.1535-1543.

View Download

SPATIAL DYNAMICS IN A PREDATOR-PREY MODEL WITH BEDDINGTON-DEANGELIS FUNCTIONAL RESPONSE

Authors:

Dridhiti Roy,Paritosh Bhattacharya,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00003

Abstract:

The dynamical behavior between predator and prey has been a dominant theme in ecology and mathematical ecology for a long time. In this paper, we look into the dynamics of the Beddington-DeAngelis predator-prey model. We reduce the equations by nondimensionalizing them and combining the spatial factor. Then we incorporate a prey refuge into the system. The model system is then subjected to homogeneous Neumann boundary conditions and the homogeneous equilibria of the full spatial model are being found.

Keywords:

Beddington-DeAngelis functional response,Beddington-DeAngelis predator-prey model,prey refuge,stability,reaction-diffusion predator-prey model,

Refference:

I. B. Ermentrout, Stripes or spots? Nonlinear effects in bifurcation of reaction–diffusion equations on the square, Proc. Roy. Soc. London 434 (1991) 413–417.

II. E. Gonz ́alez-Olivares and R. Ramos-Jiliberto, Consequences of prey refuge use on the dynamics of some simple predator–prey models: Enhancing stability?, in Proc. Third Brazilian Symp. Mathematical and Computational Biology (E-Papers Servicos Editoriais, 2004), pp. 75–98.

III. F. Chen, L. Chen and X. Xie, On a Leslie–Gower predator–prey model incorporating a prey refuge, Nonlinear Anal. Real World Appl. 10 (2009) 2905–2908.

IV. J. R. Beddington, Mutual interference between parasites or predators and its effect on searching efficiency, J. Anim. Ecol. 44 (1975) 331–340.

V. J. D. Murray, Mathematical Biology (Springer-Verlag, Berlin, 1993).

VI. M. R. Garvie, Finite difference schemes for reaction–diffusion equations modeling predator–prey interactions in MATLAB, Bull. Math. Biol. 69 (2007) 931–956.
VII. M. R. Garvie, Finite difference schemes for reaction–diffusion equations modeling predator–prey interactions in MATLAB, Bull. Math. Biol. 69 (2007) 931–956.

VIII. P. H. Crowley and E. K. Martin, Functional responses and interference within and between year classes of a dragonfly population, J. North Amer. Benthol. Soc. 8 (1989) 211–221.

IX. Wikipedia

X. X.-C. Zhang, G.-Q. Sun and Z. Jin, Spatial dynamics in a predator–prey model with Beddington–DeAngelis functional response, Phys. Rev. E 85 (2012) 021924

XI. X. Guan, W. Wang and Y. Cai, Spatiotemporal dynamics of a Leslie–Gower predator–prey model incorporating a prey refuge, Nonlinear Anal. Real World Appl. 12 (2011) 2385–2395.

View Download

NEW CONCEPTS OF T2 SEPARATION AXIOMS IN SUPRA FUZZY TOPOLOGICAL SPACE USING QUASI COINCIDENCE SENSE

Authors:

Lalin Chowdhury,Sudipto Kumar Shaha,Ruhul Amin,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00004

Abstract:

Sometimes we need to minimize the conditions of topology for different reasons such as obtaining more convenient structures to describe some real-life problems or constructing some counterexamples which show the interrelations between certain topological concepts or preserving some properties under fewer conditions of those on topology. To contribute to this research area, in this paper, we establish some notions of  separation axioms in supra fuzzy topological space in a quasi-coincidence sense. Also, we investigate some of its properties and establish certain relationships among them and other such concepts. Moreover, some of their basic properties are examined. The concept of separation axioms is one of the most important parts of fuzzy mathematics, mainly modern topological mathematics, which plays an important role in modern networking systems.

Keywords:

Fuzzy Set,Fuzzy Topology,Supra Fuzzy Topology,Quasi-coincidence,Initial and Final Supra Fuzzy Topology,

Refference:

I. Azad K. K., On Fuzzy Semi-continuous, Fuzzy Almost Continuity and Fuzzy Weakly Continuity, J. Math. Anal. Appl. 82 (1), (1981), pp 14-32.
II. Chang C. L., Fuzzy Topological Space, J. Math. Anal. Appl., 24 (1968), pp 182-192.
III. Devi R., S. Sampathkumar and M. Caldes, General Mathematics, Vol. 16, N_r. 2 (2008), pp 77-84.
IV. Fora Ali Ahmd, Separations Axioms for Fuzzy Spaces, Fuzzy Sets and Systems, 33 (1989), pp 59-75.
V. Goguen T. A., Fuzzy Tychonoff Theorem, J. Math. Anal. Appl., 43 (1973), pp 734-742.
VI. Hossain M. S., and D. M. Ali, On T_2 Fuzzy Topological Space, J. Bangladesh Academy of Science, 29 (2) (2005), pp 201-208.
VII. Lowen R., Fuzzy Topological Space and Fuzzy Compactness, J. Math. Anal. Appl., 56 (1976), pp 621-633.
VIII. Ming Pao Pu and Ming Ying Liu, Fuzzy Topology II. Product and Quotient Spaces, J. Math. Anal. Appl., 77 (1980), pp 20-37.
IX. Shen J., Separation Axioms in Fuzzifying Topology, Fuzzy Sets and Systems, 57 (1993), pp 111-123.
X. A. K., and D. M. Ali, A Comparision of Some FT_2 Concepts, Fuzzy Sets and Syystems, 23 (1987), pp 289-294.
XI. Wong C. K., Fuzzy Points and Local Properties of Fuzzy Topology, J. Math. Anal. Appl., 46 (1974), pp 316-328.
XII. Wong C. K., Fuzzy Topology: Product and Quotient Theorem, J. Math. Anal. Appl., 45 (1974) , pp 512-521.
XIII. Wuyts P., and R. Lowen, On Separation Axioms in Fuzzy Topological Spaces, Fuzzy Neighbourhood Spaces and Fuzzy Uniform Spaces, J. Math. Anal. Appl., 93 (1983), pp 27-41.
XIV. Zadeh L. A., Fuzzy Sets, Information and Control, 8 (1965), pp 338-353.

View Download

RSRW DATA, CSP AND CYCLONE TRACK PREDICTION

Authors:

Indrajit Ghosh,Sukhen Das,Nabajit Chakravarty,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00005

Abstract:

Tropical cyclones are gradually becoming an increasing menace to the coastal human civilization throughout the World. This is due to their increased frequency and intensity of occurrence nowadays. With the global increase of sea surface temperature a marked increase in the percentage of their formation from depression happening especially in the tropical oceans of the World. The Coromandel Coast of India is not an exception to these. To mitigate their devastation effect on mankind we need to study the details of their dynamics governing equations and hence develop suitable solutions. In this paper the numerical value of a stability parameter, viz. CSP is determined employing the RSRW data of one tropical cyclone that has hit the Coromandel Coast of India in 2010. CSP is a dimensionless parameter that we obtained from the analytic solution of cyclone dynamics governing equations.

Keywords:

CSP,Radial velocity,Cross-radial velocity,RSRW,Cyclone eye,Tropical cyclone,

Refference:

I. Baisya H, Pattanaik S, Chakraborty T (2020) A coupled modeling approach to understand ocean coupling and energetics of tropical cyclones in the Bay of Bengal basin. Atmospheric Research, 246, 105092. https://doi.orgII/10.1016/j.atmosres.2020.105092.
II. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686-688. https://doi:10.1038/nature03096.
III. Ghosh I, Chakravarty N (2018) Tropical cyclones: expressions for velocity components and stability parameter. Natural Hazards, 94, 1293-1304. https://doi:10.1007/s11069-018-3477-7.
IV. Goff CG, Chan JCH, Goff J, Gadd P (2016) Late holocene record of environmental changes, cyclones and tsunamis in a coastal lake, Mangania, Cook Islands Arc. 25, 333-349. https://doi:10.1111/iar.12153.
V. Köhle MP, Promper C, Glade T (2016) A common methodology for risk assessment and mapping of climate change related hazards- implications for climate change adaptation policies. MDPI Article Climate, 4, 8. https://doi:10.3390/cli4010008.
VI. Lala S, Chakravarty N, Das MK (2014) Mathematical explanation of earlier dissipation of the energy of tilted cyclone. Journal of Climatology & Weather Forecasting, 2, 113. https://doi.org/10.4172/2332-2594.1000115.
VII. Nott JF (2003) Intensity of prehistoric tropical cyclones. Journal of Geophysical Research, 108, D7 4212. https://doi.org/10.1029/2002JD002726.
VIII. Posada R, Ortega GE, Sanchez JL, Lopez L (2012) Verification of the MM5 model using radiosonde data from Madrid-Barajas Airport. Atmospheric Research, 122, 174-182. https://doi.org/10.1016/j.atmosres.2012.10.018
IX. Rezapour M (2015) A new methodology of classification of tropical cyclones: the importance of rainfall. https://espace.library.uq.edu.au/view/UQ:384798/s42713083-final-thesis.pdf. Accessed 2015.
X. Ritchie l, Vigh JL (2010) Tropical cyclone structure and intensity change: Inner core impacts Rapporteur Report, topic 1.2 conference paper. https://doi:10.13140/2.1.1825.8247
XI. Shevtsov BM, Ekaterina P, Holzworth RH (2015) Relation of tropical cyclone structure with thunder storm activity. Conference paper. https://doi:10.1117/12.2203348.
XII. Stern DP, Vigh JL, Zhang F (2015) Revisiting the relationship between eyewall contraction and intensification. Journal of Atmospheric Science, 72, 1283-1306. https://doi:10.1175/ JAS-D-14-0261.1
XIII. Tapiador FJ (2008) Hurricane footprints in global climate models. Entropy, 10, 613-620. https://doi:10.3390/e10040613.
XIV. Zehnder JA (2019) Tropical cyclone. https://www.britannica.com/science/tropicalcyclone (2018). Accessed 2020.

View Download

TIME SERIES ANALYSIS MODELING AND FORECASTING OF GROSS DOMESTIC PRODUCT OF PAKISTAN

Authors:

Nasir Saleem,Atif Akbar,A. H. M. Rahmatullah Imon,Abu Sayed Md Al Mamun,

DOI NO:

https://doi.org/10.26782/jmcms.2022.02.00006

Abstract:

The purpose of this study was to forecast the Gross Domestic Product (GDP) of Pakistan. GDP of Pakistan was observed and analyzed by using time series analysis techniques and Box-Jenkins methodology. These methods were used for analysis, estimation, and forecasting purposes. Data of GDP of Pakistan was collected from (1961-2020). The model selected had the lowest Akaike Information Criteria (AIC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Percentage Error (MPE), Schwarz Bayesian Information Criteria (SBIC), Schwarz Bayesian Criteria (SBC), values and high R2. It was used for forecasting the GDP of Pakistan for the next 55 years from 2021-to 2075. Data were analyzed by using SPSS-21, Eviews-3, and Statgraphics-16. We have found that the best model is the Linear trend model. Based on this selected model, we have found that the GDP of Pakistan would become 2.51199 in 2035 and would become less in 2075 as compared to 2025.

Keywords:

AIC,Linear Trend Model,Time Series Models,Gross,Domestic Product,Forecasting,

Refference:

I. Ahmed, V., & Amjad, R. (1984). The management of Pakistan’s economy 1947-82.
II. Badmus, M. A., & Ariyo, O. S. (2011). Forecasting cultivated areas and production of maize in Nigerian using ARIMA Model. Asian Journal of Agricultural Sciences, 3(3), 171-176.
III. Aprigliano, V., Ardizzi, G., & Monteforte, L. (2019). Using payment system data to forecast economic activity. 60th issue (October 2019) of the International Journal of Central Banking.
IV. Aastveit, K. A., Albuquerque, B., & Anundsen, A. K. (2020). Changing supply elasticities and regional housing booms.
V. Adnan, Z., Chowdhury, M., & Mallik, G. (2019). Foreign direct investment and total factor productivity in South Asia. Theoretical & Applied Economics, 2(2).
VI. Bybee, L., Kelly, B. T., Manela, A., & Xiu, D. (2020). The structure of economic news (No. w26648). National Bureau of Economic Research.
VII. Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509-1526.
VIII. Carlsen, M., & Storgaard, P. E. (2010). Dankort payments as a timely indicator of retail sales in Denmark (No. 66). Danmarks Nationalbank Working Papers.
IX. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society: Series A (Statistics in Society), 158(3), 419-444.
X. Dashti, H. M., Al-Zaid, N. S., Mathew, T. C., Al-Mousawi, M., Talib, H., Asfar, S. K., & Behbahani, A. I. (2006). Long term effects of ketogenic diet in obese subjects with high cholesterol level. Molecular and cellular biochemistry, 286(1), 1-9.
XI. DeLeeuw, J. (1992). Introduction to Akaike (1973) information theory and an extension of the maximum likelihood principle. In Breakthroughs in statistics (pp. 599-609). Springer, New York, NY.
XII. Falki, N. (2009). Impact of foreign direct investment on economic growth in Pakistan. International Review of Business Research Papers, 5(5), 110-120
XIII. Gilbert, J. M., & Balouchi, F. (2008). Comparison of energy harvesting systems for wireless sensor networks. International Journal of automation and computing, 5(4), 334-347.
XIV. Ghosh Dastidar, S., Mohan, S., & Chatterji, M. (2013). The relationship between public education expenditure and economic growth: The case of India.
XV. Galbraith, J. W., & Tkacz, G. (2018). Nowcasting with payments system data. International Journal of Forecasting, 34(2), 366-376.
XVI. Gujarati, D. N., Bernier, B., & Bernier, B. (2004). Econométrie (pp. 17-5). Brussels: De Boeck.
XVII. Hussain, S., Siddique, S., & Saboor, Q. A. (2003). Heart rate variability in early phase of acute myocardial infarction and convalescence. Journal of the College of Physicians and Surgeons–pakistan: JCPSP, 13(2), 67-69.
XVIII. Jain, A., & Pennacchiotti, M. (2010, August). Open entity extraction from web search query logs. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010) (pp. 510-518).
XIX. Liewkhim, (2005). Time series modeling and forecasting of black papper prices
XX. Makridakis, C., & Nochetto, R. H. (2003). Elliptic reconstruction and a posteriori error estimates for parabolic problems. SIAM journal on numerical analysis, 41(4), 1585-1594.
XXI. Murry, D. A., & Nan, G. D. (1994). A definition of the gross domestic product-electrification interrelationship. The Journal of energy and development, 19(2), 275-283.
XXII. McLeod, A. I., & Hipel, K. W. (1978). Simulation procedures for Box‐Jenkins models. Water Resources Research, 14(5), 969-975.
XXIII. Mallick, L., Das, P. K., & Pradhan, K. C. (2016). Impact of educational expenditure on economic growth in major Asian countries: Evidence from econometric analysis. Theoretical & Applied Economics, 23(2).
XXIV. Mastromarco, C., & Zago, A. (2012). On modeling the determinants of TFP growth. Structural Change and Economic Dynamics, 23(4), 373-382.
XXV. Nelson, C. R. (1972). The prediction performance of the FRB-MIT-PENN model of the US economy. The American Economic Review, 62(5), 902-917.
XXVI. Nihan, N. L., & Holmesland, K. O. (1980). Use of the Box and Jenkins time series technique in traffic forecasting. Transportation, 9(2), 125-143.
XXVII. Rahmaddi, R., & Ichihashi, M. (2011). Exports and economic growth in Indonesia: A causality approach based on multi-variate error correction model. 17(2), 53-73.
XXVIII. Raju, S., & Balakrishnan, M. (2019). Nowcasting economic activity in India using payment systems data. Journal of Payments Strategy & Systems, 13(1), 72-81.
XXIX. Rodrigues, M. T. D. (2017). Efeitos de um programa domiciliário de exercício físico de oito semanas em pacientes com fibromialgia (Doctoral dissertation).
XXX. Schmitz, A., & Watts, D. G. (1970). Forecasting wheat yields: an application of parametric time series modeling. American Journal of Agricultural Economics, 52(2), 247-254.
XXXI. Thavaneswaran, A., & Abraham, B. (1988). Estimation for non‐linear time series models using estimating equations. Journal of Time Series Analysis, 9(1), 99-108.
XXXII. Tomić, S., & Dressel, M. (2015). Ferroelectricity in molecular solids: a review of electrodynamic properties. Reports on Progress in Physics, 78(9), 096501.
XXXIII. Thorsrud, L. A. (2020). Words are the new numbers: A newsy coincident index of the business cycle. Journal of Business & Economic Statistics, 38(2), 393-409.
XXXIV. Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John wiley & sons.
XXXV. Tsay, A. A. (2002). Risk sensitivity in distribution channel partnerships: implications for manufacturer return policies. Journal of Retailing, 78(2), 147-160.
XXXVI. Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John wiley & sons.

View Download