Archive

Abstract:

This work presents the design of a reconfigurable cache memory utilizing a 32-bit MIPS processor, implemented through the utilization of VHDL (Very high-speed IC Hardware Description Language). A comprehensive implementation of a 32-bit, single-cycle MIPS processor is presented in VHDL. This processor is capable of supporting 50 instructions, comprising 25-R-type, 16-I-type, and 9-J-type instructions. The processor incorporates a three-dimensional reconfigurable cache memory architecture, enabling the modification of cache memory size, cache organization (in terms of associativity), and cache block size. The reconfigurable cache memory concept proposes the use of multi-cache controller units to execute reconfiguration operations and ensure that all permitted cache memory sets can be utilized with varying levels of associativity. The design is built on an FPGA using the Xilinx ISE design suite, which is based on the hardware description language. To ensure that assess functionality of the proposed reconfigurable cache memory design for various reconfiguration scenarios, numerous assembly language programs have been developed and performed on the MIPS processor. The results validated the efficacy of the suggested reconfigurable cache memory concept and demonstrated its simulation using the Xilinx ISim simulator.

Keywords:

Cache memory,MIPS processor,VHDL language,Reconfiurable cache,FPGA,

Refference:

I. AbdulAmeer, Sabah Auda, et al. “Cyber Security Readiness in Iraq: Role of the Human Rights Activists.” International Journal of Cyber Criminology 16.2 (2022): 1-14.
II. D. Camarmas-Alonso, F. García-Carballeira, E. Del-Pozo-Puñal and A. C. Mateos, “A new generic simulator for the teaching of assembly programming,” 2021 XLVII Latin American Computing Conference (CLEI), Cartago, Costa Rica, 2021, pp. 1-9, doi: 10.1109/CLEI53233.2021.9640144.
III. D. Page, “A Practical Introduction to Computer Architacture”, London, UK: Springer- Verlag, 2009.
IV. D. Sweetman, “See MIPS Run”, 2nd ed., San Francisco, USA: Morgan Kaufmann, 2007.
V. H. S. Mehta, “Design of MIPS Processor”, MSc. Thesis, California State University Northridge, California, USA, 2012.
VI. I. Lokegaonkar, D. Nair and V. Kulkarni, “Enhancement of Cache Memory Performance,” 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021, pp. 1490-1492, doi: 10.1109/ICAC3N53548.2021.9725639.
VII. J. Colmenar, J. Alvareza and J. Martinb, “Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems”. In The Journal of System and Software, Vol. 111, pp. 200-212, 2016.
VIII. J. L. Hennessy, D. A. Patterson, “Computer Organization and Design: The Hardware/Software Interface”, 4th ed., Waltham, USA: Morgan Kaufmann, 2012.
IX. J. L. Hennessy, D. A. Patterson, “Computer Architecture: A Quantitative Approach”, 5th ed., San Francisco, USA: Morgan Kaufmann, 2012.
X. J. Park, J. Lee and S. Kim, “A Way-Filtering-Based Dynamic Logical-Associative Cache Architecture for Low-Energy Consumption”. IEEE Transactions on Very Large Scale Integration (VLSI) System, Vol. 25, Issue 3, pp. 793-805, 2017.
XI. J. Pereira, “Educational package based on the MIPS architecture for FPGA platforms”, MSc. Thesis, University of Porto, Portugal, 2009.
XII. K. Kumar, M. Bharathi and S. Hariprasad, “Reconfigurable cache Implementation on FPGA”. International Journal of Scientific & Engineering Research, Vol. 4, Issue 7, pp. 1924-1928, July 2013.
XIII. K. Sundararajan, M. Jones and N. Topham, “Smart cache: A self adaptive cache architecture for energy efficiency”. In International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, pages 41-50, July 2011.
XIV. M. B. Ibne Reaz, et al., “A Single Clock Cycle MIPS RISC Processor Design using VHDL”, IEEE International Conference on Semiconductor Electronics (ICSE2002), Penang, Malaysia, PP. 126 – 129, DEC. 2002.
XV. Mezaal, Yaqeen S., et al. “Cloud computing investigation for cloud computer networks using cloudanalyst.” Journal of Theoretical and Applied Information Technology, 96(20), 2018.
XVI. M. C. Altiniğneli, “Pipelined Design Approach to Microprocessor Architectures a Partial Implementation: MIPS™ Pipelined Architecture on FPGA”, MSc. Thesis, Middle East Technical University, Turkey, 2005.
XVII. M. Linder, M. Schmid, “Processor Implementation in VHDL”, MSc. thesis, University of Ulster, Augsburg, Germany, 2007.
XVIII. P. Dandamudi, Fundamentals of Computer Organization and Design. 2nd ed. New York, USA: Springer, 2002.
XIX. R. Anjana, G. Krunal, “ VHDL Implementation of a MIPS RISC Processor”, International Journal of Advanced Research in Computer Science and software Engineering, Vol. 2, No.8, PP.83-88, 2012.
XX. R. Bhargavi and T. Sudarshan, “Design Space Exploration of Cache Memory –A Survey”. In International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 2294-2297, 2016.
XXI. R. Srinidhi “MIPS Processor Implementation”, MSc. Thesis, California State University Northridge, California, USA, 2012.
XXII. Roshani, Saeed, et al. “Filtering power divider design using resonant LC branches for 5G low-band applications.” Sustainability 14.19 (2022): 12291.
XXIII. R. S. Balpande, R. S. Keote, “Design of FPGA based Instruction Fetch & Decode Module of 32-bit RISC (MIPS) Processor”, International Conference on Communication Systems and Network Technologies (CSNT 2011), Katra, Jammu, PP. 409 – 413, Jun 2011.
XXIV. “Simplescalar”, http://www.simplescalar.com.
XXV. Shareef, M. S., Mezaal, Y. S., Sultan, N. H., Khaleel, S. K., Al-Hillal, A. A., Saleh, H. M., … & Al-Majdi, K. (2023). Cloud of Things and fog computing in Iraq: Potential applications and sustainability. Heritage and Sustainable Development, 5(2), 339-350.
XXVI. S. T, V. R. V and R. S. R., “Calculator Interface Design in Verilog HDL Using MIPS32 Microprocessor,” 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, 2022, pp. 126-129. 10.1109/WiSPNET54241.2022.9767133.
XXVII. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “New dual band dual-mode microstrip patch bandpass filter designs based on Sierpinski fractal geometry.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXVIII. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “A novel design of two loosely coupled bandpass filters based on Hilbert-zz resonator with higher harmonic suppression.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXIX. Yaqeen S. Mezaal, & Abdulkareem, S. F. (2018, May). New microstrip antenna based on quasi-fractal geometry for recent wireless systems. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
XXX. V. Robio, “A FPGA Implementation of A MIPS RISC Processor for Computer Architecture Education”, MSc. thesis, New Mexico State University, Las Cruses, New Mexico, America, 2004.
XXXI. V. R. Wadhankar, V. Tehre, “A FPGA Implementation of a RISC Processor for Computer Architecture”, National Conference on Innovative Paradigms in Engineering & Technology (NCIPET-2012), Nagpur, India, PP. 24-28, 2012.

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Abstract:

This study presents a novel two-wing armature electromagnetic launcher design that aims to overcome the limitations of conventional quadrupole railguns. The proposed TWAEL system exhibits significantly enhanced magnetic field properties, achieving a 23-fold increase in magnetic flux density, an increase in magnetic field intensity, and a 1.6-fold increase in current density compared to the QRL under identical input conditions. Consequently, the TWAEL demonstrates a 6.7-fold greater electromagnetic co-energy storage capacity per unit volume, translating to substantially higher accelerative forces, reaching up to 18,045 N. Simulations validate the TWAEL's superior performance in terms of magnetic field characteristics and energy conversion efficiency, highlighting its potential as an advanced, high-performance electromagnetic propulsion system for various applications.

Keywords:

Compressive Strength,GGBS,Metakaoline,Regression Analysis,Split Tensile Strength,

Refference:

I. Du, Xiangyu, et al. “Design and experimental study of a curved contact quadrupole railgun.” , vol. 11, no. 19, 28 Sep. 2022, p. 3108. 10.3390/electronics11193108.
II. Jamison, A., K., and R.E. Stearns. “Electrical performance of a round bore, augmented, quadrupole railgun.” , vol. 33, no. 1, 1 Jan. 1997, p. 560-565. 10.1109/20.560074.
III. Karpagam, R., et al. “Understanding the behaviour of magnetic field distribution of railgun under transient conditions using finite element method.” , vol. 31, 1 Feb. 2024, p. 100971. 10.1016/j.measen.2023.100971.
IV. Kumar, Pradeep. Studies and optimisation of pulsed power components and armature of electromagnetic railgun.
V. Liu, Shaowei, et al. “Investigation of Electromagnetic Characteristic in Series-Connected Augmented Quadrupole Rail Launcher.” Institute of Electrical and Electronics Engineers, vol. 48, no. 1, 1 Jan. 2020, p. 299-304. 10.1109/tps.2019.2960023.
VI. Liu, Shaowei, et al. “Research on transient contact characteristics of the hyperbolic rail augmented quadrupole launcher.” , vol. 50, no. 10, 1 Oct. 2022, p. 3794-3801. 10.1109/tps.2022.3205048.
VII. Liu, Yong, et al. Analysis and expansion of measuring method for rail heat in electromagnetic railgun. 1 Jan. 2024, p. 1-8. 10.1109/tps.2024.3424686.
VIII. Ma, Jiahe, et al. Finite element analysis of current density distribution in the sliding contact interface of electromagnetic railguns: A literature review. 1 Jan. 2024, p. 1-1. 10.1109/access.2024.3414648.
IX. Majumder, Balo, Dipjyoti, et al. “Effect of driving current profile on acceleration efficiency of electromagnetic railgun.” , vol. 14, no. 8, 1 Aug. 2024. 10.1063/5.0214320.
X. Mao, Wang, et al. “Analysis of the research progress of electromagnetic railgun based on CiteSpace.” , vol. 12, 1 Jan. 2024, p. 3499-3513. 10.1109/access.2023.3349028.
XI. Praneeth, Naga, R., S., and Bhim Singh. “Investigations on taper angle variation and its effects on electromagnetic railgun parameters.” , vol. 52, no. 4, 1 Apr. 2024, p. 1481-1485. 10.1109/tps.2024.3395360.
XII. Prasad, G, Prasad, G, and Kondamudi Srichandan. Performance evaluation on two wing ring type armature with inductive type electromagnetic launching system. 25 Nov. 2022, p. 1-5. 10.1109/piicon56320.2022.10045133.
XIII. Qiao, Zhiming, et al. “Check for updates multi-stage LCR square-wave circuit as practical pulsed power supply for electromagnetic railgun.” , vol. 3, p. 174.
XIV. Rodriguez, Josiah, et al. Design and simulation of an electromagnetic railgun for high velocity impact testing.
XV. Shao, K.R., et al. Numerical calculation of contact resistance of electromagnetic railgun. 1 Jan. 2024, p. 441-451. 10.1007/978-981-97-1420-9_49.
XVI. Shu, Tao, et al. “Comparison and analysis of electromagnetic characteristics of new quadrupole track electromagnetic launcher.” , vol. 711, no. 1, 1 Jan. 2020, p. 012004. 10.1088/1757-899x/711/1/012004.
XVII. Wen, Ruihu, et al. Comprehensive electromagnetic protection method of radio fuze for electromagnetic railgun. p. 0.
XVIII. Wu, Kongwei, et al. “Investigation on the microstructure evolution and mechanical properties of electromagnetic rail material in a launch environment.” , vol. 208, 1 Feb. 2024, p. 113582. 10.1016/j.matchar.2023.113582.
XIX. Yang, Zhiyong, et al. “An electromagnetic rail launcher by quadrupole magnetic field for heavy intelligent projectiles.” , vol. 45, no. 7, 1 Jul. 2017, p. 1095-1100. https://doi.org/10.1109/tps.2016.2646377.
XX. Yang, Zhiyong, et al. “An Electromagnetic Rail Launcher by Quadrupole Magnetic Field for Heavy Intelligent Projectiles.” Institute of Electrical and Electronics Engineers, vol. 45, no. 7, 1 Jul. 2017, p. 1095-1100. 10.1109/tps.2016.2646377.
XXI. Yang, Zhiyong, et al. Performances of a large scale quadrupole railgun. 1 Dec. 2017, p. 853-857. 10.1109/icctec.2017.00189.
XXII. Manohar, Kintali, et al. “A Comprehensive design and simulation of quadrupole electromagnetic linear systems for precise positioning in aerospace.” , vol. 19, no. 6, 11 Jun. 2024, 10.26782/jmcms.2024.06.00006.
XXIII. Manohar, Kintali, and Kondamudi Srichandan. “Analysis of quadrupole magnetic field reluctance-based launcher with different coil switching patterns.” , vol. 51, no. 5, 1 May. 2023, p. 1370–1376. 10.1109/tps.2023.3266515.
XXIV. Srichandan, Kondamudi, et al. “A novel type coil-multipole field hybrid electromagnetic launching system.” , vol. 15, 1 Dec. 2019, p. 102786. 10.1016/j.rinp.2019.102786.
XXV. Srichandan, Kondamudi, and Mallikarjuna Rao Pasumarthi. “Computations of magnetic forces in multipole field electromagnetic launcher.” , vol. 4, no. 3, 1 Jun. 2019, p. 761. 10.33889//ijmems.2019.4.3-059.
XXVI. Kondamudi, Srichandan, et al. “Design and analysis of cored type multipole field electromagnetic launcher (C-MFEML).” , vol. 8, p. 391–395.

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Abstract:

Burnt clay bricks are widely used across India and remain one of the most essential materials in building construction. However, the excessive extraction of clay poses a threat to society, as brick kilns largely rely on high-quality clay sourced from agricultural land. To counter this issue this study evaluates the compressive strength and microstructural characteristics of pond ash-induced compressed interlocking bricks made using fly ash, pond ash, quarry dust, and varying amounts of lime and cement. Four different mixes (S1 to S4) were tested for compressive strength after 28 days, with the S2 mix containing 10% lime and no cement achieving the highest strength of 5.48 N/mm². SEM analysis showed a dense microstructure in S2, while S1 exhibited unreacted fly ash and calcium hydroxide, resulting in a lower strength of 3.94 N/mm². XRD results confirmed the presence of calcium silicate hydrate (CSH) gel in S2, responsible for the enhanced strength. EDAX analysis highlighted the highest calcium content in S2, further indicating the extensive pozzolanic reactions leading to better densification. The study confirms that the use of lime alone, in the absence of cement, can result in higher compressive strength through improved microstructural development.

Keywords:

Fly ash,Interlocking Blocks,Microstructure,Pond ash,

Refference:

I. Al-Fakih, Amin, et al. “Characteristic compressive strength correlation of rubberized concrete interlocking masonry wall.” Structures. Vol. 26. Elsevier, 2020. 10.1016/j.istruc.2020.04.010
II. Al-Fakih, Amin, et al. “Development of interlocking masonry bricks and its’ structural behaviour: A review paper.” IOP Conference Series: Earth and Environmental Science. Vol. 140. No. 1. IOP Publishing, 2018. 10.1088/1755-1315/140/1/012127
III. Annual report, Central Electricity Authority, Ministry of Power, Government of India, New Delhi, (2022-2023). https://cea.nic.in/wp-content/uploads/annual_reports/2023/Approved_CEA_Annual_Report_2022_23.pdf
IV. Balamuralikrishnan, R., and J. Saravanan. “Effect of addition of alccofine on the compressive strength of cement mortar cubes.” Emerging Science Journal 5.2 (2021): 155-170. 10.1016/j.cscm.2023.e01968
V. Bazzar, Kaoutar, Fatima Zahra Hafiane, and Adil Hafidi Alaoui. “The early age strength improvement of the high volume fly ash mortar.” Civil Engineering Journal 7.8 (2021): 1378-1388. 10.28991/cej-2021-03091731
VI. Bhavsar, Jay K., and Vijay Panchal. “Ceramic waste powder as a partial substitute of fly ash for geopolymer concrete cured at ambient temperature.” Civil Engineering Journal 8.07 (2022). 10.28991/CEJ-2022-08-07-05
VII. Chanda, Dabashis. A Study on Socio Demographic & Health Condition of Brick Field Workers in Different Areas of Bangladesh. Diss. East West University, 2016. http://dspace.ewubd.edu/handle/2525/1995
VIII. Ghosh, Ambarish, and Chillara Subbarao. “Hydraulic conductivity and leachate characteristics of stabilized fly ash.” Journal of Environmental Engineering 124.9 (1998): 812-820. 10.1061/(ASCE)0733-9372(1998)124:9(812)

IX. Ghosh, Prasenjit, and Sudha Goel. “Physical and chemical characterization of pond ash.” International Journal of Environmental Research and Development 4.2 (2014): 129-134.https://www.academia.edu/download/43606636/Physical_and_chemical_characterization_o20160310-16230-288rxh.pdf
X. Gupta, Gaurav, Hemant Sood, and Pardeep Gupta. “Performance evaluation of pavement geomaterials stabilized with pond ash and brick kiln dust using advanced cyclic triaxial testing.” Materials 13.3 (2020): 553. 10.3390/ma13030553
XI. Javan, Anooshe Rezaee, et al. “Mechanical behaviour of composite structures made of topologically interlocking concrete bricks with soft interfaces.” Materials & Design 186 (2020): 108347. http://dx.doi.org/10.1016/j.matdes.2019.108347
XII. Kumar, Rinku, and Naveen Hooda. “An experimental study on properties of fly ash bricks.” International journal of research in aeronautical and mechanical engineering 2.9 (2014): 56-67. https://www.jetir.org/papers/JETIRZ006049.pdf
XIII. Lal, Dhirajkumar, Aniruddha Chatterjee, and Arunkumar Dwivedi. “Investigation of properties of cement mortar incorporating pond ash–an environmental sustainable material.” Construction and Building Materials 209 (2019): 20-31. 10.1016/j.conbuildmat.2019.03.049
XIV. Mohamad, Habib Musa, et al. “Manufacture of concrete paver block using waste materials and by-products: a review.” GEOMATE Journal 22.93 (2022): 9-19. 10.21660/2022.93.j2363
XV. Prakash, K. Shyam, and Ch Hanumantha Rao. “Strength characteristics of quarry dust in replacement of sand.” IOP Conference Series: Materials Science and Engineering. Vol. 225. No. 1. IOP Publishing, 2017. 10.1088/1757-899X/225/1/012074
XVI. Rath, B., Shirish Deo, and G. Ramtekkar. “Durable glass fiber reinforced concrete with supplimentary cementitious materials.” International Journal of Engineering 30.7 (2017): 964-971. 10.5829/ije.2017.30.07a.05
XVII. Romeekadevi, M., and K. Tamilmullai. “Effective Utilization of Fly Ash and Pond Ash in High Strength Concrete.” International Journal of Engineering Research & Technology (IJERT)-special issue 3 (2015): 1-7 10.17577/IJERTCONV3IS04063.
XVIII. Safiee, Nor Azizi, et al. “Structural behavior of mortarless interlocking load bearing hollow block wall panel under out-of-plane loading.” Advances in structural engineering 14.6 (2011): 1185-1196. http://dx.doi.org/10.1260/1369-4332.14.6.1185
XIX. Sarkar, Raju, and A. R. Dawson. “Economic assessment of use of pond ash in pavements.” International Journal of Pavement Engineering 18.7 (2017): 578-594. 10.1080/10298436.2015.1095915
XX. Sarkar, Ritwik, Nar Singh, and Swapan Kumar Das. (2007) “Effect of addition of pond ash and fly ash on properties of ash—clay burnt bricks.” Waste management & research, Vol 25, No 6, 566-571. 10.1177/0734242X07080114
XXI. Shafabakhsh, Gholamali, and Saeed Ahmadi. (2016) “Evaluation of coal waste ash and rice husk ash on properties of pervious concrete pavement.” International Journal of Engineering, Vol 29, No 2, 192-201. 10.5829/idosi.ije.2016.29.02b.08
XXII. Sharmin, Shaila, Wahidul K. Biswas, and Prabir K. Sarker. “Exploring the Potential of Using Waste Clay Brick Powder in Geopolymer Applications: A Comprehensive Review.” Buildings 14.8 (2024): 2317. 10.3390/buildings14082317
XXIII. Sonawane, Prashant G., Arun Kumar Dwivedi, and P. Ash (2013). “Technical properties of pond ash-clay fired bricks–an experimental study.” American Journal of Engineering Research (AJER), Vol 2, No 9, 110-117. https://www.academia.edu/download/32048746/P029110117.pdf
XXIV. Thanoon, Waleed A., et al. “Development of an innovative interlocking load bearing hollow block system in Malaysia.” Construction and Building Materials 18.6 (2004): 445-454. 10.1016/j.conbuildmat.2004.03.013
XXV. Tripathy, A., & Acharya, P. K. (2024). Strength, life cycle analysis, embodied energy and cost-sensitivity assessment of sugarcane bagasse ash-based ternary blends of geopolymer concrete. European Journal of Environmental and Civil Engineering, 28(3), 591-610. 10.1080/19648189.2023.2219709
XXVI. Udgata,Gaurav.,Sahoo, Kirtikanta., Biswal, Dipti., Sahoo, Subham.(2024) “SUSTAINABLE INVESTIGATION ON POND ASH-INDUCED COMPRESSED INTERLOCKING BRICKS.” Journal of Mechanics of Continua and mathematical sciences, Vol-19,No-6 . 10.26782/jmcms.2024.06.00003
XXVII. Verma, Aditya, et al. “Utilization of pond ash as partial replacement of cement concrete mix.” International Research Journal of Engineering and Technology (IRJET) 3.3 (2016). https://www.irjet.net/archives/V3/i3/IRJET-V3I3108.pdf
XXVIII. Vidhya, K., and S. Kandasamy. “Experimental investigations on the properties of coal-ash brick units as green building materials.” International Journal of Coal Preparation and Utilization 36.6 (2016): 318-325. 10.1080/19392699.2015.1118379
XXIX. Vidhya, K., et al. “Experimental studies on pond ash brick.” J. Eng. Res. Develop 6.5 (2013): 6-11. http://www.ijerd.com/paper/vol6-issue5/B06050611.pdf
XXX. Vidhya, K., Kandasamy, K., Karthikeyan, S. R., & SathickBasha, G. (2013). Experimental studies on pond ash brick. J. Eng. Res. Develop, 6(5), 6-11. https://www.ijerd.com/paper/vol6-issue5/B06050611.pdf
XXXI. Vijay Adhithya A., Kalpana V. G., and Malavan K. (2021) “Development and performance evaluation of interlocking bricks using industrial waste materials.” International Journal of Advance Research, Ideas and Innovations in Technology, Volume 7, Issue 4 – V7I4-1207.https://www.ijariit.com/manuscript/development-and-performance-evaluation-of-interlocking-bricks-using-industrial-waste-materials/

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Abstract:

This study focuses on estimating the shape and scale parameters of the Topp-Leone Exponential distribution. Bayes estimators are obtained using Exponential, Gamma, LogNormal, and Weibull distributions as the identical priors under asymmetric loss functions such as LINEX and Entropy and integrated with the Lindley approximation method. The simulation study was employed to determine identical prior and loss functions for the shape and scale parameters. It is observed that LINEX loss function with Exponential-Exponential prior for the shape parameter and the scale parameter Gamma-Gamma prior is most preferred for this distribution.

Keywords:

Asymmetric Loss Functions,Bayes Estimate,Lindley’s Approximation,MSE,Prior,

Refference:

I. Al-Shomrani, A., Arif O, Ibrahim, S., Hanif, S., and Shahbaz, M. “Topp–Leone Family of Distributions: Some Properties and Application.” Pakistan Journal of Statistics and Operation Research, vol. 12, no.3, 2016, pp. 443-451, doi.org/10.18187/pjsor.v12i3.1458.

II. Anitta, SA., and Dais George. ‘Bayesian Analysis of Two Parameter Weibull Distribution using Different Loss Functions.” Stochastic Modeling and Applications, vol. 24, no.2, 2020, https://www.mukpublications.com/resources/6_FT13_Final.pdf.

III. Epstein, B., and Sobel M. “Some theorems to life resting form an exponential distribution.” Annals of Mathematical Statistics, vol. 25, no.2, 1954, pp. 373-381, doi: 10.1214/aoms/1177728793.

IV. Metiri, Farouk, Zeghdoudi, Halim, and Riad Remiata, Mohamed .”On Bayes estimates of Lindley distribution under Linex loss function: Informative and Non-informative priors.” Global Journal of Pure and AppliedMathematics, vol. 12,2016, pp. 391- 400, https://api.semanticscholar.org/CorpusID:46201692.

V. Olayode, Fatoki.”The Topp-Leone Rayleigh Distribution with Application.” American Journal of Mathematics and Statistics, vol.9, no.6, 2019, pp. 215-220, 10.5923/j.ajms.20190906.02.

VI. Fithriani, I., Hakim, Arief, and Novita, Mila. “A comparison of the Bayesian method under symmetric and asymmetric loss functions to estimate the shape parameter K of Burr distribution.” Journal of Physics Conference Series, (2019),1218, 2019, 10.1088/1742-6596/1108/1/012053

VII. Genc, A. “Moments of order statistics of Topp Leone distribution.”Statistical Papers, vol.53, no.1,2012, pp.117-131, 10.1007/s00362-010-0320-y.

VIII. Albderia, Kadhim, Jawad , Hind.”Estimate survival function of the Topp-Leone exponential distribution with an application.” International journal Nonlinear Analysis and Applications, vol.12, no.2, 2021, pp: 53-60, 10.22075/ijnaa.2021.5014.
IX. Kawsar, F., and Ahmed, SP. “Bayesian Approach in Estimation of shape parameter of Exponentiated moment Exponential distribution.” Journal of Statistical Theory and Applications, vol. 17, no.2, 2017, pp.359-374, 10.2991/jsta.2018.17.2.13

X. Kotz, S., and Seier, E. “Kurtosis of the Topp Leone distributions.”. Interstat,2007, pp.1-15, https://www.researchgate.net/publication/228417010_Kurtosis_of_the_Topp-Leone_distributions.

XI. Lindley, DV. “Approximate Bayesian methods, Journal of Statistical Computation and Simulation.” Trabajos de Estadistica y de Investigacion Operativa vol. 31, 1980, pp. 223-245, http://eudml.org/doc/40822.

XII. Mohammed, H., and Khan, Ali, Athar, AbuJarad. “Bayesian Survival Analysis of Topp-Leone Generalized Family with Stan.” International Journal of Statistics and Applications, vol. 8, no.5, 2018, pp. 274-290, 10.5923/j.statistics.20180805.06.

XIII. Rasheed, Noman. “Topp–Leone compound Rayleigh distribution: properties and applications.” Research Journal of Mathematical and Statistical Science, vol. 7, no.3,2019, pp. 51–58, https://www.researchgate.net/publication/335987957_Topp-Leone_Compound_Rayleigh_Distribution_Properties_and_Applications

XIV. Nadarajah, S., and Kotz,S. “Moments of some J-shaped distributions.”Journal of Applied Statistics, vol. 30, no.3, 2003, pp. 311-317, 10.1080/0266476022000030084.

XV. Singh, Randhir. “Bayesian estimation of the unknown parameter and reliability of the Exponential distribution with a non-natural conjugate prior.” Journal of Emerging Technologies and Innovative Research, vol 8, 2021, pp. 228-236, https://www.jetir.org/papers/JETIR2109426.

XVI. Singh, SK., Singh, Umesh, and Kumar, Dinesh. “Estimation of parameters and reliability function of Exponentiated Exponential distribution: Bayesian approach under General Entropy loss function.” Pakistan Journal of Statistics and Operation Research, vol. 7,2011, pp.199-216, 10.18187/pjsor.v7i2.239.

XVII. Saridha, D., Radha, RK., and Venkatesan, P. “Bayesian estimation of Topp-Leone Exponential distribution using symmetric loss functions for identical priors.” Sirjana Journal. vol. 54, No.3,2024, pp: 19-26, 10.0015.SRJ.2024.V54I2.0096781.222.

XVIII. Topp. CW., and Leone, FC. “A family of J-shaped frequency functions.”.Journal of the American Statistical Association, vol. 50, 1955, pp.209-219, 10.1080/01621459.1955.10501259.

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Abstract:

Many studies on manufacturing and inventory management for increasingly problematic commodities have focused on how quality, greenness, product warranties, and environmental concerns might be incorporated into industrial operations. However, comparatively few articles have integrated these two themes. To close this knowledge gap and advance practice, these two decaying object criteria must be included in the study. The following assumptions are used to create a warranty-based, sustainable production inventory model for green products.  In this article, we look at how greenness, product assurances, and reducing carbon emissions technologies impact overall profit to assist those making decisions to make better choices regarding pricing and replenishment. Due to the consideration of fuzzy valued inventory parameters along with the objective function's nonlinearity and the presence of five decision variables, we have addressed this problem with the help of the Lingo 18 software along with the defuzzification technique. The mathematical formula is quantitatively demonstrated in one experiment, and a sensitivity analysis is performed to determine the effects of various factors on overall profit and management insights.

Keywords:

Fuzzy Production model,Fuzzy valued demand,Green level of the product,Triangular fuzzy number,

Refference:

I. Bai, Qingguo, Mingzhou Jin, and Xianhao Xu. “Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory.” International Journal of Production Economics 208 (2019): 83-99.
II. Battini, Daria, Alessandro Persona, and Fabio Sgarbossa. “A sustainable EOQ model: Theoretical formulation and applications.” International Journal of Production Economics 149 (2014): 145-153.
III. Bazan, Ehab, Mohamad Y. Jaber, and Ahmed MA El Saadany. “Carbon emissions and energy effects on manufacturing–remanufacturing inventory models.” Computers & Industrial Engineering 88 (2015): 307-316.
IV. Cárdenas-Barrón, Leopoldo Eduardo, et al. “An EOQ inventory model with nonlinear stock dependent holding cost, nonlinear stock dependent demand and trade credit.” Computers & Industrial Engineering 139 (2020): 105557.
V. Chen, Liang-Ho, and Liang-Yuh Ouyang. “Fuzzy inventory model for deteriorating items with permissible delay in payment.” Applied Mathematics and Computation 182.1 (2006): 711-726.
VI. Das, Subhajit, et al. “An application of control theory for imperfect production problem with carbon emission investment policy in interval environment.” Journal of the Franklin Institute 359.5 (2022): 1925-1970.
VII. Duary, Avijit, et al. “Advance and delay in payments with the price-discount inventory model for deteriorating items under capacity constraint and partially backlogged shortages.” Alexandria Engineering Journal 61.2 (2022): 1735-1745.
VIII. Dutta, Debashis, and Pavan Kumar. “Fuzzy inventory model for deteriorating items with shortages under fully backlogged condition.” International Journal of Soft Computing and Engineering (IJSCE) 3.2 (2013): 393-398.
IX. Halat, Kourosh, and Ashkan Hafezalkotob. “Modeling carbon regulation policies in inventory decisions of a multi-stage green supply chain: A game theory approach.” Computers & Industrial Engineering 128 (2019): 807-830.
X. Hou, Kuo-Lung, Li-Chiao Lin, and Yung-Fu Huang. “Integrated inventory models with process quality improvement under imperfect quality and carbon emissions.” 2016 IEEE International Conference on Mobile Services (MS). IEEE, 2016.
XI. Hou, Kuo-Lung, Li-Chiao Lin, and Yung-Fu Huang. “Integrated inventory models with process quality improvement under imperfect quality and carbon emissions.” 2016 IEEE International Conference on Mobile Services (MS). IEEE, 2016.
XII. Hua, Guowei, T. C. E. Cheng, and Shouyang Wang. “Managing carbon footprints in inventory management.” International journal of production economics 132.2 (2011): 178-185.
XIII. Jaber, Mohamad Y., Christoph H. Glock, and Ahmed MA El Saadany. “Supply chain coordination with emissions reduction incentives.” International Journal of Production Research 51.1 (2013): 69-82.
XIV. Kazemi, Nima, et al. “Economic order quantity models for items with imperfect quality and emission considerations.” International Journal of Systems Science: Operations & Logistics 5.2 (2018): 99-115.
XV. Khan, M. A. A., Halim, M. A., AlArjani, A., Shaikh, A. A., & Uddin, M. S. (2022). Inventory management with hybrid cash-advance payment for time-dependent demand, time-varying holding cost and non-instantaneous deterioration under backordering and non-terminating situations. Alexandria Engineering Journal, 61(11), 8469-8486.
XVI. Kim, Min-Soo, et al. “An improved way to calculate imperfect items during long-run production in an integrated inventory model with backorders.” Journal of Manufacturing Systems 47 (2018): 153-167.
XVII. Kumar, Sanju, Ravish Kumar Yadav, and Aashish Singh. “Study on fuzzy inventory model for deteriorating items with recurring seasonal demand pattern.” International Journal of Mathematics in Operational Research 24.3 (2023): 408-424.
XVIII. Kumar, Sharad, et al. “Sustainable fuzzy inventory model for deteriorating item with partial backordering along with social and environmental responsibility under the effect of learning.” Alexandria Engineering Journal 69 (2023): 221-241.
XIX. Kumar, Sharad, et al. “Sustainable fuzzy inventory model for deteriorating item with partial backordering along with social and environmental responsibility under the effect of learning.” Alexandria Engineering Journal 69 (2023): 221-241.
XX. quantity and market demand in dedicated or combined remanufacturing systems.” Applied Mathematical Modelling 64 (2018): 135-167.
XXI. Lin, Tien-Yu, and Bhaba R. Sarker. “A pull system inventory model with carbon tax policies and imperfect quality items.” Applied Mathematical Modelling 50 (2017): 450-462.
XXII. Manna, Amalesh Kumar, et al. “A fuzzy imperfect production inventory model based on fuzzy differential and fuzzy integral method.” Journal of Risk and Financial Management 15.6 (2022): 239.
XXIII. Manna, Amalesh Kumar, et al. “A fuzzy imperfect production inventory model based on fuzzy differential and fuzzy integral method.” Journal of Risk and Financial Management 15.6 (2022): 239.
XXIV. Manna, Amalesh Kumar, et al. “Modeling of a carbon emitted production inventory system with interval uncertainty via meta-heuristic algorithms.” Applied Mathematical Modelling 106 (2022): 343-368.
XXV. Manna, Amalesh Kumar, et al. “Modeling of a carbon emitted production inventory system with interval uncertainty via meta-heuristic algorithms.” Applied Mathematical Modelling 106 (2022): 343-368.
XXVI. Mishra, U., Wu, J. Z., Tsao, Y. C., & Tseng, M. L. (2020). Sustainable inventory system with controllable non-instantaneous deterioration and environmental emission rates. Journal of Cleaner Production, 244, 118807.
XXVII. Mishra, Umakanta, Jei-Zheng Wu, and Biswajit Sarkar. “A sustainable production-inventory model for a controllable carbon emissions rate under shortages.” Journal of Cleaner Production 256 (2020): 120268.
XXVIII. Mishra, Umakanta, Jei-Zheng Wu, and Biswajit Sarkar. “Optimum sustainable inventory management with backorder and deterioration under controllable carbon emissions.” Journal of Cleaner Production 279 (2021): 123699.
XXIX. Oberthür, Sebastian. The Kyoto Protocol: International Climate Policy for the 21st Century. Springer, 1999.
XXX. Rahman, Md Sadikur, et al. “An application of real coded Self-organizing Migrating Genetic Algorithm on a two-warehouse inventory problem with Type-2 interval valued inventory costs via mean bounds optimization technique.” Applied Soft Computing 124 (2022): 109085.
XXXI. Rahman, Md Sadikur, et al. “Interval valued demand related inventory model under all units discount facility and deterioration via parametric approach.” Artificial Intelligence Review (2022): 1-40.
XXXII. Rani, Smita, Rashid Ali, and Anchal Agarwal. “Fuzzy inventory model for new and refurbished deteriorating items with cannibalisation in green supply chain.” International Journal of Systems Science: Operations & Logistics 9.1 (2022): 22-38.
XXXIII. Rout, Chayanika, et al. “Cooperative sustainable supply chain for deteriorating item and imperfect production under different carbon emission regulations.” Journal of cleaner production 272 (2020): 122170.
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XXXV. Sarkar, Biswajit, et al. “Combined effects of carbon emission and production quality improvement for fixed lifetime products in a sustainable supply chain management.” International Journal of Production Economics 231 (2021): 107867.
XXXVI. Shi, Yan, et al. “Optimal replenishment decisions for perishable products under cash, advance, and credit payments considering carbon tax regulations.” International Journal of Production Economics 223 (2020): 107514.
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XXXVIII. Tiwari, Sunil, et al. “Joint pricing and inventory model for deteriorating items with expiration dates and partial backlogging under two-level partial trade credits in supply chain.” International Journal of Production Economics 200 (2018): 16-36.
XXXIX. Tiwari, Sunil, et al. “Retailer’s optimal ordering policy for deteriorating items under order-size dependent trade credit and complete backlogging.” Computers & Industrial Engineering 139 (2020): 105559.
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Abstract:

Public officials face a challenge when illustrating and assessing strategies for developing active communities, necessitating a thorough examination of inter-ministerial collaboration. This research is focused on introducing a Cohesive fuzzy graph that portrays cooperation and contributing factors between various ministries. The current graph can adeptly convert complex information by utilizing two variables, thereby enhancing the fidelity and effectiveness of hesitant fuzzy graphs in representing both information and phases. We demonstrate a cohesive fuzzy graph's practical application using the ideas of score function and deviation degree.

Keywords:

Cohesive fuzzy graph,Complex fuzzy graph,Fuzzy graph,Hesitant fuzzy graph. ,

Refference:

I. AbuHijleh, Eman A. “Complex hesitant fuzzy graph.” Fuzzy Information and Engineering 15.2 (2023): 149-161. 10.26599/FIE.2023.9270010.

II. Alkouri, Abdulazeez (Moh’D. Jumah) S., and Abdul Razak Salleh. “Complex intuitionistic fuzzy sets.” AIP conference proceedings. Vol. 1482. No. 1. American Institute of Physics, 2012. 10.1063/1.4757515.

III. Atanassov, Krassimir T., and Krassimir T. Atanassov. Intuitionistic fuzzy sets. Physica-Verlag HD, 1999. 10.1016/S0165-0114(86)80034-3.

IV. Chen, Na, Zeshui Xu, and Meimei Xia. “Interval-valued hesitant preference relations and their applications to group decision making.” Knowledge-based systems 37 (2013): 528-540. 10.1016/j.knosys.2012.09.009.

V. Javaid, Muhammad, Agha Kashif, and Tabasam Rashid. “Hesitant fuzzy graphs and their products.” Fuzzy Information and Engineering 12.2 (2020): 238-252. 10.1080/16168658.2020.1817658.

VI. Karaaslan, Faruk. “Hesitant fuzzy graphs and their applications in decision making.” Journal of Intelligent & Fuzzy Systems 36.3 (2019): 2729-2741. 10.3233/JIFS-18865.
VII. Pathinathan, T., J. Jon Arockiaraj, and J. Jesintha Rosline. “Hesitancy fuzzy graphs.” Indian Journal of Science and Technology 8.35 (2015): 1-5. 10.17485/ijst/2015/v8i35/86672.

VIII. Ramot, Daniel, et al. “Complex fuzzy sets.” IEEE transactions on fuzzy systems 10.2 (2002): 171-186. 10.1109/91.995119.

IX. Rosenfeld, Azriel. “Fuzzy graphs.” Fuzzy sets and their applications to cognitive and decision processes. Academic press, 1975. 77-95. 10.1016/B978-0-12-775260-0.50008-6.

X. Talafha, Mohammad, et al. “Complex hesitant fuzzy sets and its applications in multiple attributes decision-making problems.” Journal of Intelligent & Fuzzy Systems 41.6 (2021): 7299-7327. 10.3233/JIFS-211156.

XI. Torra, Vicenç. “Hesitant fuzzy sets.” International journal of intelligent systems 25.6 (2010): 529-539. 10.1002/int.20418.

XII. Xue, Xingsi, et al. “On cohesive fuzzy sets, operations and properties with applications in electromagnetic signals and solar activities.” Symmetry 15.3 (2023): 595. 10.3390/sym15030595.

XIII. Yaqoob, Naveed, et al. “Complex intuitionistic fuzzy graphs with application in cellular network provider companies.” Mathematics 7.1 (2019): 35. 10.3390/math7010035.

XIV. Zadeh, Lotfi A. “Fuzzy sets.” Information and Control (1965). 10.1016/S0019-9958(65)90241-X.

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Abstract:

Mental health care is a greatly underserved aspect of the general well-being, especially in resource-lacking settings. In India, mental healthcare has faced challenges of vast untreated mental disorders within populations. Community mental health care nursing has proven to be a very promising strategy. The objective of the study is to analyze the community mental health services about the curriculum for community mental health nursing. A quantitative research approach has been adapted to accomplish this by using a community-based survey research design. A total of 200 nursing service workers including Nursing Officers & CHO have been selected by following a multistage sampling strategy from four selected districts of Odisha. Demonstrating gradual improvements with experience, practice scores had a mean ± SD and median (IQR) each with a significant p-value of 0.015. It was found that most of the cases (56%) had a good or very good level of knowledge of mental health nursing, but 45.5% had good or very good practice in mental health nursing. The study revealed that the knowledge and the practice of the service gap of mental health were minimal. The majority of participants displayed good knowledge and sound practice. Recommendations by the nursing personnel included in-service education and training programmes to improve their knowledge along with the provision of adequate numbers of staff to manage the cases efficiently.

Keywords:

Community Mental Health Nursing,Community Nursing Officers,Knowledge,Practice,

Refference:

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XXXII. Rehm J, Shield KD (2019) Global burden of disease and the impact of mental and addictive disorders. Current Psychiatry Reports 21(2) 10.1007/s11920- 019-0997-0
XXXIII. Rentala, Sreevani1; Thimmajja, Sunanda Govinder2,; Vasudevareddy, Savitha S.3; Srinivasan, P.4; Desai, Mahesh5. Impact of Mental Health First Aid Training for Primary Health Care Nurses on Knowledge, Attitude and Referral of Mentally Ill Patients. Indian Journal of Continuing Nursing Education 23(2):p 184-189, Jul–Dec 2022. | DOI: 10.4103/ijcn.ijcn_62_22
XXXIV. Risjord MW (2010) Nursing knowledge: Science, practice and philosophy. Oxford, Wiley Blackwell, United Kingdom 33(2): 129-131.
XXXV. Sagar R, Dandona R, Gururaj G, Dhaliwal RS, Singh A, Ferrari A et al (2020) The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990– 2017. Lancet Psychiatry 7(2):148–161. 10.1016/S2215-0366(19)30475-4
XXXVI. Saraceno B, van Ommeren M, Batniji R, Cohen A, Gureje O, Mahoney J. Barriers to improvement of mental health services in low-income and middle-income countries. Lancet. 2007;370:74.
XXXVII. Sibeko G, Milligan PD, Roelofse M, et al. Piloting a Mental Health Training Programme for Community Health Workers in South Africa : An Exploration of Changes in Knowledge. 2018:1–10. confidence and attitudes.
XXXVIII. Siv Camilla Marriott, Ellen Karine Grov, Marianne Thorsen Gonzalez, Learning and achieving basic mental health competence in placement studies with the support of a tool: A qualitative study of student nurses’ experiences, International Journal of Nursing Studies Advances, Volume 7,2024,100219, ISSN 2666-142X, 10.1016/j.ijnsa.2024.100219.
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XLI. X. Q., & Ruan, J. (2022). Experiences and challenges faced by community mental health workers when providing care to people with mental illness: a qualitative study. BMC psychiatry, 22(1), 623. 10.1186/s12888-022-04252-z

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Abstract:

Detecting moving objects in dense foggy conditions is a challenging problem in computer vision, with critical applications in smart transportation systems, surveillance, and autonomous driving. Fog particles scatter and absorb light, significantly reducing visibility and making it difficult for traditional computer vision algorithms to accurately detect moving objects. To address this challenge, researchers have proposed learning-based approaches that leverage deep neural networks to recognize moving objects and adapt to the unique characteristics of foggy environments. In this study, we present a learning-based method utilizing the YOLOv7 framework to effectively detect moving objects in dense fog conditions. The proposed approach involves four key stages: feature extraction, feature fusion, object detection, and non-maximum suppression. The results achieved are highly promising when compared to state-of-the-art techniques.

Keywords:

Moving Object Detection,Bad Weather,Foggy Environment,YOLO,YOLOv7,Profound Learning,Neural Network,

Refference:

I. Ahmed, Muhammad, et al. “Survey and performance analysis of deep learning based object detection in challenging environments.” Sensors 21.15 (2021): 5116. 10.3390/s21155116

II. Bijelic, Mario, et al. “Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. 10.1109/CVPR42600.2020.01170

III. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. “Yolov4: Optimal speed and accuracy of object detection.” arXiv preprint arXiv:2004.10934 (2020). 10.48550/arXiv.2004.10934

IV. Chaturvedi, Saket S., Lan Zhang, and Xiaoyong Yuan. “Pay” Attention” to Adverse Weather: Weather-aware Attention-based Object Detection.” 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. 10.1109/ICPR56361.2022.9956149

V. Dong, Nan, Zhen Jia, Jie Shao, Zhipeng Li, Fuqiang Liu, Jianwei Zhao, and Pei-Yuan Peng. “Adaptive Object Detection and Visibility Improvement in Foggy Image.” Journal of Multimedia, vol.6, no. 1, 2011. doi:10.4304/jmm.6.1.14-21
VI. Farhadi, Ali, and Joseph Redmon. “Yolov3: An incremental improvement.” Computer vision and pattern recognition. Vol. 1804. Berlin/Heidelberg, Germany: Springer, 2018. 10.48550/arXiv.1804.02767

VII. Horvat, Marko, Ljudevit Jelečević, and Gordan Gledec. “A comparative study of YOLOv5 models performance for image localization and classification.” Central European Conference on Information and Intelligent Systems. Faculty of Organization and Informatics Varazdin, 2022.

VIII. Hsu, Han-Kai, et al. “Progressive domain adaptation for object detection.” Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2020. 10.1109/WACV45572.2020.9093358

IX. Huang, Shih-Chia, Trung-Hieu Le, and Da-Wei Jaw. “DSNet: Joint semantic learning for object detection in inclement weather conditions.” IEEE transactions on pattern analysis and machine intelligence 43.8 (2020): 2623-2633. 10.1109/tpami.2020.2977911
X. Krišto, Mate, Marina Ivasic-Kos, and Miran Pobar. “Thermal object detection in difficult weather conditions using YOLO.” IEEE access 8 (2020): 125459-125476. 10.1109/ACCESS.2020.3007481

XI. Kumar, Prashant, et al. “Detection of video objects in dynamic scene using local binary pattern subtraction method.” Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 2. Springer India, 2015.

XII. Li, Chuyi, et al. “YOLOv6: A single-stage object detection framework for industrial applications.” arXiv preprint arXiv:2209.02976 (2022). 10.48550/arXiv.2209.02976

XIII. Liu, Wenyu, et al. “Image-adaptive YOLO for object detection in adverse weather conditions.” Proceedings of the AAAI conference on artificial intelligence. Vol. 36. No. 2. 2022. 10.1609/aaai.v36i2.20072

XIV. Nie, Xin, Meifang Yang, and Ryan Wen Liu. “Deep neural network-based robust ship detection under different weather conditions.” 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019. 10.1109/ITSC.2019.8917475

XV. Nithyanandham, Lalithamani. “Obstacle Detection of Vehicles under Fog.” International Journal of Simulation–Systems, Science & Technology 20.1 (2019).

XVI. Redmon, J. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. 10.1109/CVPR.2016.91

XVII. Rout, Deepak Kumar, and Sharmistha Puhan. “A spatio-temporal framework for moving object detection in outdoor scene.” International Conference on Computing and Communication Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. https://doi.org/10.1007/978-3-642-29216-3_54

XVIII. Rout, Deepak Kumar, and Sharmistha Puhan. “Video object detection using inter-frame correlation based background subtraction.” 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE, 2013. 10.5120/14255-2364

XIX. Sen, Prithwish, Anindita Das, and Nilkanta Sahu. “Object detection in foggy weather conditions.” International Conference on Intelligent Computing & Optimization. Cham: Springer International Publishing, 2021. 10.1007/978-3-030-93247-3_70

XX. Sindagi, Vishwanath A., et al. “Prior-based domain adaptive object detection for hazy and rainy conditions.” Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. Springer International Publishing, 2020. 10.1007/978-3-030-58568-6_45

XXI. Sharma, Teena, et al. “Deep learning-based object detection and scene perception under bad weather conditions.” Electronics 11.4 (2022): 563. 10.3390/electronics11040563

XXII. Walambe, Rahee, et al. “Lightweight object detection ensemble framework for autonomous vehicles in challenging weather conditions.” Computational Intelligence and Neuroscience 2021.1 (2021): 5278820. 10.1155/2021/5278820

XXIII. Zou, Zhengxia, et al. “Object detection in 20 years: A survey.” Proceedings of the IEEE 111.3 (2023): 257-276. 10.48550/arXiv.1905.05055

XXIV. Zhao, Zhong-Qiu, et al. “Object detection with deep learning: A review.” IEEE transactions on neural networks and learning systems 30.11 (2019): 3212-3232. 10.1109/TNNLS.2018.2876865

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Abstract:

This article presents projections of the future population growth in Bangladesh and Sri Lanka, along with a comparative analysis of the demographics of these two countries. Based on our projections, the projected population of Bangladesh in 2060 is estimated to be around 211 million, while the population of Sri Lanka is projected to be around 356 million. The exponential expansion of the global population represents one of the most formidable challenges facing humanity, profoundly impacting individual well-being while compelling transformative adaptations within societal frameworks and governmental institutions. Moreover, we have computed a forecast for the next period and included a demographic evaluation of the rural populations and crime rates of both nations in this piece. This study critically examines the precision and applicability of three mathematical frameworks, namely, the least squares model, the logistic growth model, and the Malthusian (exponential growth) model, in forecasting population dynamics within Bangladesh and Sri Lanka by the conclusion of the twenty-first century. Even though transgender individuals are recognized as the third gender, the appropriate statistics are not yet available. In addition, the study presents a speculative analysis of how the state has addressed the growth of its population in previous instances and how it would address it in coming years.

Keywords:

Crime Report and Rural People,Mean Absolute Percentage Error,Population Model,

Refference:

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Ⅱ. AL MAMUN, H. A. S. A. N., et al. “ANALYZING AND PROJECTION OF FORECASTING POPULATION OF BANGLADESH USING EXPONENTIAL MODEL, LOGISTIC MODEL, AND DISCRETE LOGISTIC MODEL.” 10.17605/OSF.IO/S5UCD.
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Ⅴ. Cocks, Edmond. “Malthus on population quality.” Social Biology 18.1 (1971): 84-87, doi: 10.1080/19485565.1971.9987904.
Ⅵ. Cohen, Joel E. “Population growth and earth’s human carrying capacity.” Science 269.5222 (1995): 341-346, doi: 10.1126/science.7618100.
Ⅶ. Ehrlich, Isaac, and Francis Lui. “The problem of population and growth: A review of the literature from Malthus to contemporary models of endogenous population and endogenous growth.” Journal of Economic Dynamics and Control 21.1 (1997): 205-242, 10.1016/0165-1889(95)00930-2.
Ⅷ. Feeney, Griffith, and Iqbal Alam. “New estimates and projections of population growth in Pakistan.” Population and development review 29.3 (2003): 483-492, 10.1111/j.1728-4457.2003.00483.x.
Ⅸ. Henson, Shandelle M. “Mathematical models in population biology and epidemiology.” (2003): 254-258, 10.2307/3647954.
Ⅹ. Hsieh, Shun-Chieh. “Analyzing urbanization data using rural–urban interaction model and logistic growth model.” Computers, Environment and urban systems 45 (2014): 89-100, 10.1016/j.compenvurbsys.2014.01.002.
Ⅺ. Islam, Rashedul, et al. “Proliferation of stem cells in a population model.” Afr. J. Bio. Sci. 6.5 (2024): 2305-2328, 10.33472/AFJBS.6.5.2024.2305-2328.
Ⅻ. Karim, ANM Rezaul, et al. “Modeling on population growth and its adaptation: A comparative analysis between Bangladesh and India.” Journal of Applied and Natural Science 12.4 (2020): 688-701. 10.31018/jans.v12i4.2396.
XIII. Karim, Rezaul, et al. “A study about the prediction of population growth and demographic transition in Bangladesh.” Journal of Umm Al-Qura University for Applied Sciences (2024): 1-13, 10.1007/s43994-024-00150-0.
XIV. Karim, Rezaul, et al. “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 5.5 (2020): 77-83, doi: 10.22271/maths.2020.v5.i5b.585.
XV. Karim, Rezaul, et al. “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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XXVII. Wali, Augustus N., Doriane Ntubabare, and Vedaste Mboniragira. “Mathematical modeling of Rwanda’s population growth.” (2011).

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Abstract:

This article discusses the design reliability and quality of dairy products. Reliability and stochastic analysis of a dairy plant divided into two units using the regenerative method. At startup time, both units are in active mode. If one of the units fails, the system remains in active mode but goes into a reduced state. If both units fail, the system enters an invalid state. The distribution of the repair time is arbitrary in this system, whereas the malfunctioning time is exponentially distributed. With the data gathered from the milk plant, we assess average time to system failure, availability, busy time, and profitability through a mathematical analysis in this work that makes use of the Laplace transform, semi-Markov process, and regenerative point. Consequently, the conclusion of this paper helps plant managers make timely maintenance decisions and enhance the system’s overall performance.

Keywords:

Average time to system failure,reliability,Laplace transform,busy time,

Refference:

I. Adlakha, N., Taneja, G. and Batra, S. (2017). Reliability and cost-benefit analysis of a twounit cold standby system used for communication through satellite with assembling andactivation time. Int. J. Appl. Eng. Res., 12, pp.: 9697–9702. https://www.ripublication.com/ijaer17/ijaerv12n20_59.pdf

II. Ahmadini, A.A.H., Singla, S., Mangla, D., Modibbo, U.M. and Rani, S., 2024. Reliability Assessment and Profit Optimization of Multi-unit Mixed Configured System using ABC Algorithm under Preventive Maintenance. IEEE Access, Digital Object Identifier 10.1109/ACCESS.2024.3406994
III. Barak M.S., Garg R. and Kumar A. (2021), “Reliability measures analysis of a milk plant using RPGT”, Life Cycle Reliab Saf Eng 10, pp.: 295-302. 10.1007/s41872-020-00163-8
IV. Batra, S. and Taneja, G. (2018). A reliability model for the optimum number of standby units in a system working with two operative units. Ciencia e Tecnica, 33:0254-0223.

V. Batra, S. and Taneja, G. (2018). Optimization of number of hot standby units through reliability models for a system operative with one unit. International Journal of Agricultural and Statistical Sciences,14. https://www.researchgate.net/publication/329376891

VI. Batra, S. and Taneja, G. (2018). Reliability and optimum analysis for number of standby units in a system working with one operative unit. International Journal of Applied Engineering Research, 13: 2791-2797. https://www.ripublication.com/ijaer18/ijaerv13n5_93.pdf

VII. Batra, S. and Taneja, G. (2019). Reliability modeling and optimization of the number of hot standby units in a system working with two operative units. An international journal of advanced computer technology, 10:3059-3068. https://ijact.in/index.php/j/article/view/481/461

VIII. Garg R. (2020). Behavioural Analysis of single Unit System Using RPGT. Journal of Xi’an University of Architecture and Technology, pp.: 2723-2735. 20.19001.JAT.2020.XII.I2.20.2084

IX. Jain, M. and Gupta, R. (2013). Optimal replacement policy for a repairable system with multiple vacations and imperfect fault coverage. Comput. Ind. Eng., 66(4), pp. 710-719. 10.1016/j.cie.2013.09.011

X. John, Y.M., Sanusi, A., Yusuf, I. and Modibbo, U. M. (2022). Reliability analysis of multi-hardware-software system with failure interaction. J., 66, pp. 957-977. 10.47852/bonviewJCCE2202216

XI. Kumari S., Khurana P. and Singla S. (2022). Behavior and profit analysis of a thresher plant under steady state. Int J Syst Assur Eng Manag 13, pp.: 166-171. 10.1007/s13198-021-01183-y

XII. Levitin, G., Finkelstein, M., and Xiang, Y. (2020). Optimal preventive replacement for cold standby systems with elements exposed to shocks during operation and task transfers. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 10, pp.: 2168–2216. 10.1109/TSMC.2020.3034493

XIII. Mahmoud, M. A. W. and Moshref, M. E. (2009). On a two-unit cold standby system considering hardware, human error failures, and preventive maintenance. Math. Comput. Model., 51, pp.: 736–745. 10.1016/j.mcm.2009.10.019

XIV. Malhotra. and Taneja, G. (2015). Comparative study between a single unit system and a two-unit cold standby system with varying demand.Springerplus, 4, pp.: 1–17. 10.1186/s40064-015-1484-7

XV. Malhotra, R., Dureja, T., and Goyal, A. (2021). Reliability analysis a two-unit cold redundant system working in a pharmaceutical agency with preventive maintenance. In Journal of Physics: Conference Series, 10, pp.: 1742–1750. 10.1088/1742-6596/1850/1/012087

XVI. Manocha, A. and Taneja, G. (2015). Stochastic analysis of a two-unit cold standby system with arbitrary distributions for life, repair and waiting times. Int. J. Performability Eng., 11, pp.:293–299. 10.23940/ijpe.15.3.p293.mag

XVII. Manocha, A., Taneja, G. and Singh, S. (2017). Stochastic and cost-benefit analysis of two-unit hot standby database system. Int. J. Performability Eng., 13, pp.: 63–72. 10.23940/ijpe.17.01.p5.6372

XVIII. Modibbo, U. M., Arshad, M., Abdalghani, O. and Ali, I. (2021). Optimization and estimation in system reliability allocation problem. Rel. Eng. Syst. Saf., 212. 10.1016/j.ress.2021.107620

XIX. Raghav, Y.S., Mradula, Varshney, R., Modibbo, U.M, Ahmadini, ., A. A. H., and Ali, I. (2022). Estimation and optimization for system availability under preventive maintenance. IEEE Access, 10, pp.: 94337-94353. 10.1109/ACCESS.2022.3204394

XX. Singla, S. and Rani, S., 2023, November. Performance Optimization of 3: 4:: Good System. Second International Conference on Informatics (ICI) IEEE, pp.: 1-4. https://iciconference.org/

XXI. Singla S., Mangla, D., Panwar, P. and Taj, S. Z. (2024). Reliability optimization of a degraded system under preventive maintenance using genetic algorithm,” J. Mech. Continua Math. Sci., 2024. 10.26782/jmcms.2024.01.00001

XXII. Singla S, Mangla D, Kumar M. A., Muhammad M.U. (2024). Reliability optimization methods: A systematic literature review. Yugoslav Journal of Operations Research. 10.2298/YJOR230715031S

XXIII. Singla, S., Rani, S., Modibbo, U. M. and Ali, I. (2023). Optimization of system parameters of 2:3 good serial system using deep learning. Rel., Theory Appl., pp. 670-679. 10.24412/1932-2321-2023-476-670-679

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