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Abstract:

Over the past few decades, the Micro, Small, and Medium Enterprises (MSMEs) sector has emerged as a dynamic and vibrant component of the economy of India. A pivotal role is being played by MSMEs to generate noteworthy prospects in employment with relatively lower capital investment compared to large industries, while also contributing to the development of rural and underdeveloped areas. Macroeconomics plays a central role in understanding the dynamics of national and global economies by analyzing aggregate indicators such as GDP (Gross Domestic Product), inflation, unemployment, and interest rates. This research attempts to predict the MSME production based on the Macroeconomic fuzzy input variables using the ANFIS (Adaptive Neuro Fuzzy Inference) model. The time series data, such as GDP Per Capita (at constant price), Repo Rate, CRR (Cash Reserve Ratio), and CPI (Consumer Price Index), are considered as macroeconomic input variables, and the output variable is MSME Production (at constant price) for the last 20 years. The paper compares the actual value of MSME production with the ANFIS outcome and the prediction accuracy of the output variable between the same membership function (MF) usage for all the input variables and different MF usage of the input variables, with a linear output MF being observed. The prediction accuracy obtained in the latter case overcomes the prediction accuracy of the former. Accurate prediction of MSME production volume using macroeconomic variables helps policymakers envision industrial activity and design sensible fiscal and monetary measures to alleviate growth and support the MSME sector.

Keywords:

ANFIS,MSME,GDP,CRR,Repo Rate,CPI,

References:

I. Behera M, Mishra S, Mohapatra N, Behera A R. “Covid-19 pandemic and Micro Small and Medium Enterprises (MSMEs): Policy response for revival.” Small enterprises development, Management and Extension Journal, vol. 47, no. 3, 2021, pp. 213-228. 10.1177/09708464211037485
II. Chukhrova N, Johansen A. “Fuzzy Regression Analysis: Systematic Review and bibliography.” Applied Soft Computing Journal, vol. 84, 2019 pp. 1-29. 10.1016/j.asoc.2019.105708
III. Gare D. “Performance of Micro Small and Medium Enterprises of India.” International Journal of Creative Research Thoughts, vol. 10, no. 10, 2022, pp. 1-8.

IV. Gibson T, Van der Vaart, H.J. “Defining SMEs: A less Imperfect Way of defining Small and Medium Enterprises in Developing Countries.” Brookings Global Economy and Development, 2008.

V. Jovic S, Milutinovic J S, Micic R, Markovic S, Rakic G. “Analyzing of Exchange Rate and Gross Domestic Product (GDP) by Adaptive Neuro Fuzzy Inference System (ANFIS).” vol. S0378, no. 4371, 2018, pp. 31133-31136. 10.1016/j.physa.2018.09.009

VI. Khanna R, Singh S P. “Status of MSMEs in India: A detailed Study.” Journal of Applied Management – Jidnyasa, vol. 10, no.2, 2018, pp. 1-14.

VII. Melin P, Soto J, Castillo O, Soria J. “A new approach for time series prediction using ensembles of ANFIS models.” Expert Systems with Applications, vol. 39, 2012, pp. 3494-3506. 10.1016/j.eswa.2011.09.040

VIII. Palaka S, Das S. “Growth and Elasticity of output of MSMEs in India.” Research Square, 2021, pp. 1-16. 10.21203/rs.3.rs-36142/v2

IX. Patel S K, Tripathy R. “Challenges of MSMEs in India.” Journal of Positive School Psychology, vol. 6, no. 6, 2022, pp. 1-23.

X. Petkovic J, Petrovic N, Dragovic I, Stanojevic K, Radakovic J A, Borojevic T, Borstnar M K. “Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach.” PLoS One, 2019, pp. 1-25. 10.1371/journal.pone.0218855

XI. Raharaja M A, Darmawan I D M B A, Nilakusumawati D P E, Supriana I W. “Analysis of Membership Function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction.” Vol. 1722, no. 012005, 2021. 10.1088/1742-6596/1722/1/012005

XII. Rawat M. “Factors affecting the growth and development of MSME sector in India: An opinion survey of start-ups.” Mathematical Statistical and Engineering Applications, vol. 68, no. 1, 2019, pp. 230-236. 10.17762/msea.v68i1.2177

XIII. Reddy K, Sashidharan S. “Driving Small and Medium-Sized Enterprise participation in Global Value Chains: Evidences from India.” ADBI Working Paper series, vol. 1118, 2020, pp. 1-24.

XIV. Sahnewaz S. “The Contribution of MSMEs in India’s total export and GDP Growth: Evidence from cointegration and causality test.” Munich Personal RePEc Archive, 2028, pp. 1-15.

XV. Shetty M O, Bhatt G S. “A Performance analysis of Indian MSMEs.” International Journal of Applied Engineering and Management Letters, vol. 6, no. 2, 2022, pp. 1-19. 10.5281/zenodo.7112375
XVI. Shing J, Jang R. “ANFIS: Adaptive-Network-Based Fuzzy Inference System.” IEEE Transactions on Systems, MAN, and Cybernetics, vol. 23, no. 3, 1993, pp. 665-685. 10.1109/21.256541

XVII. Siva Sree H V, Vasavi P. “MSMEs in India – Growth and Challenges.” Journal of Scientific Computing, 2020, pp. 1-12.

XVIII. Tambunan T T H. “The Impact of the Economic Crisis on micros, small and medium enterprises and their crisis mitigation measures in Southeast Asia with reference to Indonesia.” Wiley Asia and Pacific policy studies, 2018, pp. 1-21. 10.1002/app5.264

XIX. Uwimana A. “Macroeconomics Dynamics through the lens of the Adaptive Neuro Fuzzy Inference Systems.” Intech Open, 2024, pp. 1-13. 10.5772/intechopen.1004041

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XXI. www.rbi.org.in. Accessed 12 Oct 2025

XXII. www.msme.gov.in. Accessed 15 Oct 2025

XXIII. www.dcmsme.gov.in. Accessed 5 Nov 2025

XXIV. www.nsic.co.in. Accessed 6 Nov 2025

XXV. www.nimsme.org. Accessed 5 Nov 2025

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XXVII. www.coirboard.gov.in. Accessed 2 Nov 2025

XXVIII. www.mgiri.org. Accessed 12 Oct 2025

XXIX. www.nseindia.com. Accessed 14 Oct 2025

XXX. www.mospi.gov.in. Accessed 8 Nov 2025

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Abstract:

This paper presents a coupled inductor-based high-gain DC-DC converter. The proposed topology achieves high step-up conversion and reduced voltage stress of the switch and output diode. The leakage energy is recycled effectively to improve converter efficiency. Moreover, huge turn-off voltage spikes of the switch caused by the leakage inductor are completely eliminated. Besides, all the diodes, except the output diode, turn off softly, eliminating the reverse recovery problem. The operating principle of the converter in continuous conduction mode (CCM) is presented. The voltage gain characteristics, CCM-DCM boundary operation, and parameter design guidelines have been elaborated. A comparison analysis with similar converters has also been presented. Finally, the theoretical analysis has been verified through a simulation study in orcad pspice software. The simulation results are found to be in close agreement with the theoretical calculations.

Keywords:

Boost converter,continuous conduction mode (CCM),coupled Inductor,discontinuous conduction mode (DCM),solar photovoltaic (SPV),zero current switching (ZCS).,

References:

I. Ayachit, S. U. Hasan, Y. P. Siwakoti, M. Abdul-Hak, M. K. Kazimierczuk and F. Blaabjerg (2019). Coupled-Inductor Bidirectional DC-DC Converter for EV Charging Applications with Wide Voltage Conversion Ratio and Low Parts Count, 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA, pp. 1174-1179. 10.1109/ECCE.2019.8912858.
II. C. -L. Chu and Y. Chen (2009). ZVS-ZCS bidirectional full-bridge DC-DC converter, 2009 International Conference on Power Electronics and Drive Systems (PEDS), Taipei, Taiwan, pp. 1125-1130. 10.1109/PEDS.2009.5385685.
III. D. Kumar, F. Zare, and A. Ghosh (2017). DC microgrid technology: Systemarchitectures, AC grid interfaces, grounding schemes, power quality, communication nnetworks, applications, and standardizations aspects, IEEE Access, vol. 5, pp. 1223012256.
IV. Finney S.J,Williams B.W, Green T.C (1996). RCD snubber revisited. 768 IEEE Trans Ind Appl 32(1):155–160. 10.1109/28.485827.
V. F. Abbasi, Aghdam Meinagh, J. Yuan, and Y. Yang (2020). Analysis and design of a high voltage-gain quasi-Z-source DC–DC converter, IET Power Electronics, early access. 10.1049/iet-pel.2019.1165.
VI. Heris PC, Saadatizadeh Z, Rostami N (2019). Transformerless quadratic-based high step-down DC–DC converter with wide duty cycle range, IET Power Electron. 12(3):368-382.
VII. H. K. Patel (2008). Voltage transient spikes suppression in flyback converter using dissipative voltage snubbers, 3rd IEEE conference on industrial electronics and applications, Singapore, 897–901. 10.1109/ICIEA.2008.4582645.
VIII. H. -L. Do (2010). A Soft-Switching DC/DC Converter With High Voltage Gain, IEEE Transactions on Power Electronics, vol. 25, no. 5, pp. 1193-1200. 10.1109/TPEL.2009.2039879.
IX. H. Terashi and T. Ninomiya (2004). Analysis of leakage-inductance effect in a flyback DC-DC converter using time keeping control, INTELEC 2004. 26th Annual International Telecommunications Energy Conference, Chicago, IL, USA, pp. 718-724. 10.1109/INTLEC.2004.1401550.
X. J. Gnanavadivel, K. Jayanthi, S. Vasundhara, K.V. Swetha& K. JeyaKeerthana (2023). Analysis and design of high gain DC-DC converter for renewable energy applications, Automatika, 64:3, 408-421. 10.1080/00051144.2023.2170062.
XI. K. Kobayashi, H. Matsuo, and Y. Sekine (2006). Novel solar-cell power supplysystem using a multiple-input DC–DC converter, IEEE Trans. Ind.Electron., vol. 53, no. 1, pp. 281–286.
XII. K. P. Bhatt, M. T. Shah, V. Patel and V. Kharbikar (2011). Effect of leakage inductance in bidirectional DC to DC converter, 2011 Nirma University International Conference on Engineering, Ahmedabad, India, pp. 1-6. 10.1109/NUiConE.2011.6153303.
XIII. Liang T-J, Huynh KKN, Chen K-H, HuangW-Y, Tran TAA (2024). Forward–flyback integrated converter with voltage clamping and soft switching, IEEE Trans Power Electron 39(10):13365–13376. 10.1109/TPEL.2024.3418491
XIV. Lin JY, Wang CF, Lin CY, Chen JL, Wang JM (2014). An active clamping zero-voltage-switching flyback converter with integrated transformer, Int J Circuit Theory Appl 43(10):1351–1366. 10.1002/cta.2009.
XV. Lish, M. H., Ebrahimi, R., Kojabadi, H. M., Guerrero, J. M., Esfetanaj, N. N., & Chang, L. (2020). Novel high gain DC–DC converter based on coupled inductor and diode capacitor techniques with leakage inductance effects. IET Power Electronics, 13(11), 2380-2389. 10.1049/iet-pel.2020.0117.
XVI. M. -K. Nguyen, T. -D. Duong and Y. -C. Lim (2018). Switched-Capacitor-Based Dual-Switch High-Boost DC–DC Converter, IEEE Transactions on Power Electronics, vol. 33, no. 5, pp. 4181-4189. 10.1109/TPEL.2017.2719040.
XVII. M. Lotfi Nejad, B. Poorali, E. Adib, and A. A. Motie Birjandi (2016). New cascade boost converter with reduced losses, IET Power Electronics, vol. 9, no. 6, pp. 1213–1219. 10.1049/iet-pel.2015.0240.
XVIII. M. M. Haji-Esmaeili, E. Babaei and M. Sabahi (2018). High Step-Up Quasi-Z Source DC–DC Converter, IEEE Transactions on Power Electronics, vol. 33, no. 12, pp. 10563-10571. 10.1109/TPEL.2018.2810884.
XIX. N. Mohan, T.M. Undeland and W.P. Robbins (2003). Power Electronics: Converters, Applications and Design, 3rd edition, Hoboken, NJ, USA, Wiley:2003, ISBN 978-0-471-22693-2.
XX. P. K. Maroti, S. Esmaeili, A. Iqbal, and M. Meraj (2020). High step-up single switch quadratic modified SEPIC converter for DC microgrid applications, IET Power Electron., vol. 13, no. 16, pp. 37173726. 10.1049/iet-pel.2020.0147.
XXI. S. K. Mazumder, R. K. Burra, and K. Acharya (2007). A ripple-mitigating andenergy-efficient fuel cell power-conditioning system, IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1437–1452, Jul. 2007.
XXII. S. Sadaf, N. Al-Emadi, P. K. Maroti and A. Iqbal (2021). A New High Gain Active Switched Network-Based Boost Converter for DC Microgrid Application, IEEE Access, vol. 9, pp. 68253-68265. 10.1109/ACCESS.2021.3077055.
XXIII. S. Saha (2021). Soft-switched high step-up DC/DC boost converter for distributed generation, International Journal of Power Electronics, vol. 13, no 1, pp. 112-131.
XXIV. Texas instruments (2020). Mitigation procedure on voltage spike of switching node from flyback converter, application report.
XXV. W. Li and X. He (2011). Review of non isolated high-step-up DC/DC converters in photovoltaic grid-connected applications, IEEE Trans. Ind. Electron.,vol. 58, no. 4, pp. 12391250.
XXVI. X. Zhu, B. Zhang and K. Jin (2020). Hybrid Nonisolated Active Quasi-Switched DC-DC Converter for High Step-up Voltage Conversion Applications, IEEE Access, vol. 8, pp. 222584-222598. 10.1109/ACCESS.2020.3043816.

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Abstract:

Background: Breast cancer survival is influenced by multiple clinical and pathological factors, and appropriate modelling is required to obtain reliable prognostic estimates while accounting for unobserved heterogeneity. Methodology: A population-based retrospective survival analysis was conducted among women diagnosed with primary breast cancer. Survival time from diagnosis to death or event was analysed using proportional hazards (PH) and accelerated failure time (AFT) models across multiple parametric distributions. Shared gamma frailty models were fitted at the age-group level to account for unobserved heterogeneity. Results: Higher tumour grade and lymph node ratio (LNR) were the strongest predictors of poor survival. Compared with grade 1 tumours, grade 3 tumours were associated with substantially shorter survival times (time ratio ?0.55 – 0.59) and increased hazard (hazard ratio ?1.8 - 1.9). Patients with LNR > 0.68 experienced markedly earlier events (time ratio ? 0.33 – 0.38) and higher hazard (hazard ratio ? 3.1). Advanced age showed the largest adverse effect, with patients older than 78.5 years experiencing events approximately three to four times earlier (time ratio ? 0.26 – 0.29). Hormone receptor-negative tumours were associated with reduced survival (time ratio ? 0.71 – 0.86). Flexible AFT models, particularly the generalized gamma distribution, demonstrated superior fit. Frailty modelling revealed moderate unobserved heterogeneity (?? 0.30), with attenuation of effect sizes but preserved inference. Conclusion: Key prognostic factors for breast cancer survival remained robust across modelling frameworks and after accounting for unobserved heterogeneity. The combined use of PH, AFT, and frailty models provides clinically interpretable and reliable survival estimates

Keywords:

Survival analysis,Cox model,Frailty model,Breast cancer,Tumour grade,Heterogeneity,

References:

I. Altman, Douglas G. Practical statistics for medical research. Chapman and Hall/CRC, 1990. 10.1201/9780429258589
II. Brandt, Jasmine, et al. "Age at diagnosis in relation to survival following breast cancer: a cohort study." World journal of surgical oncology 13.1 (2015): 33. 10.1186/s12957-014-0429-x
III. Chang, Yao-Jen, et al. "Recursive partitioning analysis of lymph node ratio in breast cancer patients." Medicine 94.1 (2015): e208. 10.1097/MD.0000000000000208
IV. Collett, David. Modelling survival data in medical research. Chapman and Hall/CRC, 2023. 10.1201/9781003282525
V. Cox, David R. "Regression models and life?tables." Journal of the royal statistical society: Series B (methodological) 34.2 (1972): 187-202. 10.1007/978-1-4612-4380-9_37

VI. Dou, He, et al. "Estrogen receptor-negative/progesterone receptor-positive breast cancer has distinct characteristics and pathologic complete response rate after neoadjuvant chemotherapy." Diagnostic Pathology 19.1 (2024): 5. 10.1186/s13000-023-01433-6
VII. Duchateau, Luc, and Paul Janssen. The frailty model. New York, NY: Springer New York, 2008. 10.1007/978-0-387-72835-3_4
VIII. Duchateau, Luc, Paul Janssen, and Steven Abrams. "Frailty Model, The." International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2025. 982-992. 10.1007/978-3-662-69359-9_694
IX. Elston, Christopher W., Ian O. Ellis, and Sarah E. Pinder. "Pathological prognostic factors in breast cancer." Critical reviews in oncology/hematology 31.3 (1999): 209-223. 10.1016/S1040-8428(99)00034-7
X. Faradmal, Javad, et al. "Survival analysis of breast cancer patients using Cox and frailty models.” Journal of research in health sciences 12.2 (2012). https://pubmed.ncbi.nlm.nih.gov/23241526/
XI. Goldhirsch, Aron, et al. “Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.” Annals of oncology 22.8 (2011): 1736-1747. 10.1093/annonc/mdr304
XII. Gorfine, Malka, and David M. Zucker. "Shared frailty methods for complex survival data: a review of recent advances." Annual Review of Statistics and Its Application 10.1 (2023): 51-73. https://doi.org/10.1146/annurev-statistics-032921-021310
XIII. Hurley, Margaret Anne. "A reference relative time-scale as an alternative to chronological age for cohorts with long follow-up." Emerging themes in epidemiology 12.1 (2015): 18. https://doi.org/10.1186/s12982-015-0043-6
XIV. Alotaibi, Refah Mohammed, and Chris Guure. “Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality.” Computational and mathematical methods in medicine vol. 2020 9076567. 8 Feb. 2020, 10.1155/2020/9076567.
XV. Kleinbaum, D.G. and Klein, M. (2012) Survival Analysis: A Self-Learning Text. 3rd Edition, Springer, New York. https://doi.org/10.1007/978-1-4419-6646-9
XVI. Liu, Dechun, et al. "Lymph node ratio and breast cancer prognosis: a meta-analysis." Breast Cancer 21.1 (2014): 1-9. 10.1007/s12282-013-0497-8
XVII. Rakha, Emad A., et al. "Prognostic significance of Nottingham histologic grade in invasive breast carcinoma." Journal of clinical oncology 26.19 (2008): 3153-3158. 10.1200/JCO.2007.15.5986
XVIII. Rakha, Emad A., et al. "Breast cancer prognostic classification in the molecular era: the role of histological grade." Breast cancer research 12.4 (2010): 207. 10.1186/bcr2607
XIX. Putter H, Van Houwelingen HC. Frailties in multi-state models: Are they identifiable? Do we need them?. Statistical methods in medical research. 2015 Dec;24(6):675-92. 10.1177/0962280211424665

XX. Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1973-2013), National Cancer Institute, DCCPS, Surveillance Research Program, Surveillance Systems Branch, released April 2016, based on the November 2015 submission. https://seer.cancer.gov/
XXI. Yazdani, Akram et al. “Investigation of Prognostic Factors of Survival in Breast Cancer Using a Frailty Model: A Multicenter Study.” Breast cancer : basic and clinical research vol. 13 1178223419879112. 29 Sep. 2019, doi:10.1177/1178223419879112
XXII. Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries." CA: a cancer journal for clinicians 71.3 (2021): 209-249. 10.3322/caac.21660
XXIII. Therneau, Terry M., and Patricia M. Grambsch. "The cox model." Modeling survival data: extending the Cox model. New York, NY: Springer New York, 2000. 39-77. 10.1007/978-1-4757-3294-8_3
XXIV. Vaupel, James W., Kenneth G. Manton, and Eric Stallard. "The impact of heterogeneity in individual frailty on the dynamics of mortality." Demography 16.3 (1979): 439-454. 10.2307/2061224
XXV. Wei, Lee-Jen. "The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis." Statistics in medicine 11.14?15 (1992): 1871-1879. 10.1002/sim.4780111409
XXVI. Wienke, Andreas. Frailty models in survival analysis. Chapman and Hall/CRC, 2010. 10.1201/9781420073911
XXVII. Yazdani, Akram, et al. "Application of Frailty Quantile Regression Model to investigate of factors survival time in breast Cancer: a Multi-center Study." Health Services Research and Managerial Epidemiology 10 (2023): 23333928231161951. 10.1177/23333928231161951

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Abstract:

This paper will consider the demographic trend of Bangladesh using population data from 1975 to 2020 in 5-year blocks and annual immigration data from 2000 to 2020, and extrapolating the data to 2100. Several growth models were used to represent nonlinear growth trends and measure the extent to which the post-2000 immigration has changed the demographic momentum. The findings indicate that there is a long-term population growth moving to a slower yet consistent increase with immigration as a secondary source of acceleration, especially in urban areas. It is estimated that with the combined effect of natural growth and ongoing immigration, Bangladesh may be at the borderline of having a much higher population density by the year 2100, with the strain on urban infrastructure, labor markets, and resource systems. The combined view gives a better glimpse of the relationship between internal growth and external inflows in the development of future demographics in the country.

Keywords:

Demographic trend,Immigration data,Growth models,Long-term population,Future demographics,

References:

I. C. S. Berkey and N. M. Laird, “Nonlinear growth curve analysis: estimating the population parameters,” Ann Hum Biol, vol. 13, no. 2, pp. 111–128, Jan. 1986. 10.1080/03014468600008261.
II. E. Cocks, “Malthus on population quality,” Soc Biol, vol. 18, no. 1, pp. 84–87, Mar. 1971. 10.1080/19485565.1971.9987904.
III. G. F. Mulligan, “Logistic Population Growth in the World’s Largest Cities,” Geogr Anal, vol. 38, no. 4, pp. 344–370, Oct. 2006. 10.1111/j.1538-4632.2006.00690.x.
IV. H. AL Mamun, E. Ali, K. Chandra Roy, R. Karim, N. Uddin, and P. Dey, “ANALYZING AND PROJECTION OF FORECASTING POPULATION OF BANGLADESH USING EXPONENTIAL MODEL, LOGISTIC MODEL, AND DISCRETE LOGISTIC MODEL”. 10.17605/OSF.IO/S5UCD.
V. H. Mondol, U. Mallick, and Md. Biswas, “Mathematical modeling and predicting the current trends of human population growth in Bangladesh,” Modelling, Measurement and Control D, vol. 39, no. 1, pp. 1–7, Dec. 2018. 10.18280/mmc_d.390101.
VI. I. Ehrlich and F. Lui, “The problem of population and growth: A review of the literature from Malthus to contemporary models of endogenous population and endogenous growth,” J Econ Dyn Control, vol. 21, no. 1, pp. 205–242, Jan. 1997. 10.1016/0165-1889(95)00930-2.
VII. M. A. Tabatabai, W. M. Eby, and K. P. Singh, “Hyperbolastic modeling of wound healing,” Math Comput Model, vol. 53, no. 5–6, pp. 755–768, Mar. 2011. 10.1016/j.mcm.2010.10.013.
VIII. M. M. Islam, “Demographic transition and the emerging windows of opportunities and challenges in Bangladesh,” J Popul Res, vol. 33, no. 3, pp. 283–305, Sep. 2016. 10.1007/s12546-016-9174-z.
IX. N. M. Rezaul Karim, M. Nizam Uddin, M. Rana, M. U. Khandaker, M. R. I. Faruque, and S. M. Parvez, “Modeling on population growth and its adaptation: A comparative analysis between Bangladesh and India,” Journal of Applied and Natural Science, vol. 12, no. 4, pp. 688–701, Dec. 2020. 10.31018/jans.v12i4.2396.
X. P. Dey et al., “QUALITATIVE ANALYSIS OF DEMOGRAPHIC PERSPECTIVE AND HUMAN POPULATION MODEL WITHIN BANGLADESH AND SRI LANKA,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 19, no. 12, pp. 139–158, Dec. 2024. 10.26782/jmcms.2024.12.00009.

XI. P. Dey et al., “SOUTH ASIAN DEMOGRAPHY: A NOVEL INSIGHT ON GROWTH RATE ACROSS THREE NATIONS,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 20, no. 10, pp. 174–199, October 2025. 10.26782/jmcms.2025.10.00011.
XII. R. Islam et al., “PROLIFERATION OF STEM CELLS IN A POPULATION MODEL,” AfricanJournalofBiological Sciences Rashedul Islam / Afr.J.Bio.Sc, vol. 6, no. 5, pp. 2305–2328, 2024. 10.33472/AFJBS.6.5.2024.
XIII. R. Islam, K. Chandra Roy, N. Uddin, R. Karim, S. Sarker, and P. Dey, “DEMOGRAPHIC ANALYSIS AND COMPARISON WITH THE POPULATION OF BANGLADESH AND PAKISTAN”. 10.5281/zenodo.8241173.
XIV. R. Karim, M. A. Akbar, M. A. B. Pk, and P. Dey, “A study on fractional-order mathematical and parameter analysis for CAR T-cell therapy for leukemia using homotopy perturbation method,” Partial Differential Equations in Applied Mathematics, vol. 14, Jun. 2025. 10.1016/j.padiff.2025.101152.
XV. R. Karim, M. A. Akbar, M. A. B. Pk, P. Dey, and M. T. Tahmed, “Mathematical analysis of chimeric antigen receptor T-cell therapy for leukaemia using optimal control approach,” Journal of Umm Al-Qura University for Applied Sciences, 2025. 10.1007/s43994-025-00219-4.
XVI. R. Karim, M. A. Arefin, Md. M. Hossain, and Md. S. Islam, “Investigate future population projection of Bangladesh with the help of Malthusian model, Sharpe-lotka model and Gurtin Mac-Camy model,” International Journal of Statistics and Applied Mathematics, vol. 5, no. 5, pp. 77–83, Sep. 2020. 10.22271/maths.2020.v5.i5b.585.
XVII. R. Karim, M. A. B. Pk, P. Dey, M. A. Akbar, and M. S. Osman, “A study about the prediction of population growth and demographic transition in Bangladesh,” Journal of Umm Al-Qura University for Applied Sciences, vol. 11, no. 1, pp. 91–103, Mar. 2025. 10.1007/s43994-024-00150-0.
XVIII. R. Karim, M. A. Bkar Pk, M. Asaduzzaman, P. Dey, and M. A. Akbar, “INVESTIGATION ON PREDICTING FAMILY PLANNING AND WOMEN’S AND CHILDREN’S HEALTH EFFECTS ON BANGLADESH BY CONDUCTING AGE STRUCTURE POPULATION MODEL,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 19, no. 3, pp. 65–86, Mar. 2024. 10.26782/jmcms.2024.03.00005.

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Abstract:

Scheduling problems represent a core challenge in the efficient management of industrial and service operations. Due to their structural complexity and significant practical relevance in both manufacturing and service sectors, Hybrid Flow Shop Scheduling Problems (HFSSPs) are widely recognized as NP-hard. Scheduling in contemporary manufacturing and production systems sometimes entails ambiguous and uncertain information, rendering classical deterministic methods less efficacious. This work presents a novel comparative analysis of the exact method Branch and Bound (BB) and heuristic algorithm Nawaz, Enscore, and Ham (NEH) for addressing the hybrid flow shop scheduling problem (HFSSP), where processing times are articulated via Interval-Valued Intuitionistic Fuzzy Sets (IVIFS). A ranking and scoring algorithm is utilised to convert IVIFS data into computationally manageable values, facilitating integration with BB and NEH methodologies. The results offer valuable insights for scheduling in uncertain and imprecise production environments, demonstrating how hybrid decision-making strategies that combine exact and heuristic methods can lead to more effective solutions.

Keywords:

Interval-Valued Intuitionistic Fuzzy Sets,Hybrid Flow Shop Scheduling,Score Function,Makespan,

References:

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Abstract:

This study addresses a significant gap in mental health research by developing a computational algorithm that goes beyond existing traditional symptom analysis. Instead of treating mental health symptoms as isolated phenomena, we created a methodology that captures their complex interconnected nature. We developed the Dynamic Weighted Centrality Hysteresis Symptoms Network Algorithm (DWCHSN), which applies concepts in network science to mental health symptomology. The DWCHSN algorithm effectively detects and ranks symptoms based on their centrality and influence that collectively capture how symptoms activate, spread, self-reinforce, persist, and respond to intervention within the network. This helps clinicians in setting treatment priorities by identifying the symptoms that are important catalysts. Our algorithm connects theoretical psychopathology models with clinical practice, unlike conventional diagnostic frameworks that list symptoms without considering their relationships.

Keywords:

Graph-Theoretic Modeling,Dynamic Centrality,Symptom Networks,Mental Health,Hysteresis,

References:

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