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Abstract:

This current study examines the effects of the use of hydrogen gas as well as nanoparticle additives on the performance, combustion, and emission characteristics of a compression ignition (CI) engine. A fixed flow rate of 2 L/min of hydrogen gas was supplied to the system, and concentrations of 25, 50, and 75 ppm of nanoparticle additives were employed. Engine performance was determined by measuring the Brake Specific Energy Consumption (BSEC) and Brake Thermal Efficiency (BTE), while the combustion efficiency was analyzed through the cylinder pressure and heat release rate (HRR). The emissions parameters of CO, HC, NOx, and Smoke opacity were also tested at varying loads of engine operations. The findings of this experimental study demonstrate significant improvement in combustion efficiency and engine performance from hydrogen gas enrichment as well as nanoparticle additives. The fuel blend with the highest BTE and lowest BSEC was H₂ at 2 L/min + 75 ppm. The combustion analysis showed that the maximum heat release rate increased from 45 J/°CA to 82 J/°CA and the maximum cylinder pressure for diesel increased from 68 bar to 90 bar. The system was supplied with hydrogen gas at a set flow rate of 2 L/min, and concentrations of 25, 50, and 75 ppm of nanoparticle additives were utilized. Significant reductions in CO, HC, and smoke opacity were achieved due to the increased combustion and oxidation processes within the fuel blends. Under full load, there was about a 30%, 24%, and 36% reduction in CO, HC, and smoke opacity, respectively. However, NOx emissions increased as a result of the increased temperatures in the cylinder due to effective combustion. Generally, the results indicated significant improvements in combustion efficiency as well as reductions in incomplete combustion pollutants due to the combination of hydrogen and nanoparticles. Based on the results, H₂ at 2 L/min + 75 ppm gave the best blend in terms of emission reductions and increased efficiency.

Keywords:

Hydrogen enrichment,Nanoparticle additives,Compression ignition engine,Brake thermal efficiency,Heat release rate,Energy Efficiency,Combustion analysis.,

References:

I. Agarwal, A. K., Dhar, A., Gupta, J. G., Kim, W. I., Choi, K., Lee, C. S., & Park, S. (2020).: Effect of fuel injection pressure and injection timing on diesel engine performance and emissions. Fuel, 111, pp. 374–383. 10.1016/j.fuel.2013.03.016
II. Ashok, B., Nanthagopal, K., Jeevanantham, A. K., Bhowmik, S., & Malhotra, D. (2021). Hydrogen-fueled compression ignition engines: Current status and future perspectives. Fuel, 305, 121561. 10.1016/j.fuel.2021.121561
III. Al-Waeli, A. H. A., Sopian, K., Kazem, H. A., Chaichan, M. T., & Ibrahim, A. (2021). Nanofluid applications in energy systems: A review. International Journal of Heat and Mass Transfer, 178, 121631. 10.1016/j.ijheatmasstransfer.2021.121631
IV. Awad, O. I., Mamat, R., Ali, O. M., Azmi, W. H., Kadirgama, K., Yusri, I. M., & Leman, A. M. (2020). Alcohol and hydrogen enrichment in diesel engines: A review. Renewable and Sustainable Energy Reviews, 79, pp. 307–320. 10.1016/j.rser.2017.05.034
V. Basha, J. S., Anand, R. B., & Jebaraj, S. (2019). Effect of alumina nanoparticles blended biodiesel fuel on diesel engine performance and emissions. Journal of Engineering for Gas Turbines and Power, 133(3), 032801. 10.1115/1.4002648
VI. Devarajan, Y., Nagappan, B., Munuswamy, D. B., & Mahalingam, A. (2022). Experimental investigation on hydrogen enrichment in compression ignition engines. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(1), pp. 324–338. 10.1080/15567036.2020.1841360
VII. Devarajan, Y., Babu, D. M., & Munuswamy, D. B. (2021). Nano-fuel additives for enhanced diesel engine performance and emissions control. Energy Reports, 7, pp. 3429–3443. 10.1016/j.egyr.2021.05.090
VIII. Elkelawy, M., Bastawissi, H. A. E., Esmaeil, K. K., Radwan, A. M., Panchal, H., Sadasivuni, K. K., & Walvekar, R. (2022). Recent developments in hydrogen-enriched diesel combustion technology. Sustainability, 14(3), 1432. 10.3390/su14031432
IX. Elumalai PV., Saleel, C. A., Gulbarga, M. I., Hussain, F., Khan, S. A., Rajendran, P., Kaewthep, C., & Yong, X. (2026). A multi-criteria sustainability and engine performance study of andropogon narudus biodiesel using the PUGH matrix and ML. Scientific Reports, 16(1). 10.1038/s41598-026-46841-y
X. Fayad, M. A. (2021). Effect of hydrogen enrichment on combustion characteristics and exhaust emissions of diesel engines. International Journal of Hydrogen Energy, 46(12), pp. 8754–8766. 10.1016/j.ijhydene.2020.12.059
XI. Gharehghani, A., Hosseini, R., Mirsalim, M., Yusaf, T., & Najafi, G. (2020). Combustion and emission characteristics of hydrogen-assisted diesel engines. Renewable Energy, 149, pp. 1254–1268. 10.1016/j.renene.2019.10.103
XII. Hosseini, S. E., & Wahid, M. A. (2020). Hydrogen production from renewable and sustainable energy resources: Promising green energy carrier for clean development. Renewable and Sustainable Energy Reviews, 57, pp. 850–866. 10.1016/j.rser.2015.12.112
XIII. Kumar, B. R., Saravanan, S., Rana, D., & Nagendran, A. (2021). Influence of nanoparticle additives on diesel engine performance and emissions: A review. Fuel, 302, pp. 121-128. 10.1016/j.fuel.2021.121128
XIV. Nayak, S. K., Mishra, P. C., & Das, L. M. (2022). Experimental analysis of hydrogen-fueled dual-fuel diesel engines. International Journal of Hydrogen Energy, 47(15), pp. 9210–9224. 10.1016/j.ijhydene.2022.01.038
XV. Saxena, V., Kumar, N., & Saxena, V. K. (2020). Comprehensive review on hydrogen-fueled compression ignition engines. Renewable and Sustainable Energy Reviews, 70, pp. 579–596. 10.1016/j.rser.2016.11.190
XVI. Sharma, P., Sharma, S., & Jain, S. (2021). Recent advances in nano-additives for diesel engine applications. Energy Conversion and Management, 245, 114566. 10.1016/j.enconman.2021.114566
XVII. Senthil Kumar, M., Ramesh, A., & Nagalingam, B. (2020). Experimental investigations on hydrogen-assisted diesel engine operation. International Journal of Hydrogen Energy, 45(32), pp. 16015–16028. 10.1016/j.ijhydene.2020.04.112
XVIII. Soudagar, M. E. M., Nik-Ghazali, N. N., Kalam, M. A., Badruddin, I. A., Banapurmath, N. R., Khan, T. Y., & Akram, N. (2021). Improvement of engine characteristics using nano-additive fuels: A review. Renewable and Sustainable Energy Reviews, 145, pp. 111-114. 10.1016/j.rser.2021.111114
XIX. Yusaf, T., Hamawand, I., Baker, P., & Najafi, G. (2021). The effect of hydrogen blending on diesel engine performance and emissions. International Journal of Automotive Technology, 22(3), pp. 705–716. 10.1007/s12239-021-0065-0
XX. Zhang, Z., Balasubramanian, R., & Sharma, B. K. (2022). Nanoparticle-enhanced fuels for internal combustion engines: A critical review. Fuel Processing Technology, 227, pp. 107-115. 10.1016/j.fuproc.2021.107115

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Abstract:

This paper introduces new properties and theorems of the Aboodh-ARA-Kamal Integral Transform (AAKIT), a generalized fractional integral transform with wide applications in Applied Mathematics, Engineering, and Mathematical Physics. We derive and prove several fundamental properties. Furthermore, new inversion and uniqueness theorems are presented with examples, demonstrating the practical use of AAKIT in solving differential and integral equations.

Keywords:

Aboodh transform,ARA transform,Integral transform,Laplace transform; Partial differential equations and Kamal transform.,

References:

I. Abdoon, M. A., Saadeh, R., Berir, M., EL Guma, F., & Ali, M. (2023), Analysis, modeling and simulation of a fractional-order influenza model. Alexandria Engineering Journal, Vol,74, 231–240. 10.1016/j.aej.2023.05.011
II. Aboodh, K., Abdullahi, I., and Nuruddeen, R. (2017), On the Aboodh transform connections with some famous integral transforms, International Journal of Engineering and Information Systems, Vol.1, 143–151. http://ijeais.org/wp-content/uploads/2017/11/IJEAIS171116.pdf
III. Aboodh, K. S. (2013), The New Integral Transform ''Aboodh Transform'', Global Journal of Pure and Applied Mathematics, Vol.9 (Number 1), pp. 35-43. http://www.ripublication.com/gjpam.htm
IV. Ahmed, S. A. (2021), Applications of new double integral transform (Laplace–Sumudu transform) in mathematical physics. In Alberto Fiorenza (Ed.), Abstract and Applied Analysis, Vol. 2021, pp. 1–8. 10.1155/2021/6625247
V. Al-Aati, A, Al-Sharai, Wedad M. and Al-Sharai, Wafa M. (2025), The Double Aboodh-Kamal Transform and Their Properties with Applications to Partial Differential Equations, Al-Saeed journal of Applied sciences, Vol 8 (No.1), 22-37.
VI. https://journal.alsaeeduni.edu.ye/index.php/SJAS/article/view/261/218
VII. Atangana, A. (2013), A note on the triple Laplace transform and its applications to some kind of third-order differential equation. In R. K. Bera (Ed.), Abstract and Applied Analysis, Vol. 2013, pp. 1–10. 10.1155/2013/769102
VIII. Atangana, A., and Kilicman, A. (2013), A novel integral operator transforms and its application to some FODE and FPDE with some kind of singularities, Mathematical Problems in Engineering, Vol. 2013, 1-7. 10.1155/2013/531984
IX. Davies, B. (2002), Integral transforms and their applications (Vol. 41). New York: Springer Science & Business Media. https://d-nb.info/963754297/04
X. Debnath, L., and Bhatta, D. (2014), Integral transforms and their applications (3rd ed.). Chapman and Hall/CRC. 10.1201/b17670
XI. Elzaki, T. M., Ahmed, S. A., Areshi, M., and Chamekh, M. (2022), Fractional partial differential equations and novel double integral transform. Journal of King Saud University – Science, 34(3), 1-7. 10.1016/j.jksus.2022.101832
XII. Pawar, D. D., Kadam, R.D., Bhuttampalle, G.G. and Ahmed, W. F. S. (2024), A study of Double ARA-Kamal Transform Properties and Applications, Advances in Dynamical Systems and Applications, Volume 19 (No.1), pp. 75-90. https://www.researchgate.net/publication/384371266_A_study_of_Double_ARA-Kamal_Transform_Properties_and_Applications
XIII. Saadeh, R., Qazza, A. and Burqan, A. (2020), A new integral transform: ARA transform and its properties and applications, Symmetry, 12(6), 925. 10.3390/sym12060925
XIV. Saadeh, R. Z. and Ghazal, B. F. A. (2021), A new approach on transforms: Formable integral transform and its applications, Axioms, Vol. 10(4), 332.
XV. 10.3390/axioms10040332
XVI. Tao, H., Anjum, N. and Yang, Y. J. (2023), The Aboodh transformation-based homotopy perturbation method: New hope for fractional calculus, Frontiers in Physics, Vol. 11, 310.   10.3389/fphy.2023.1168795

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Abstract:

The Healthcare Internet of Things (HIoT) supports real-time monitoring and clinical decision-making through interconnected medical devices and sensors. Fog computing reduces communication delay by processing data near its source, while blockchain improves security, integrity, and decentralized trust. However, filtering, encryption, authentication, queueing, and consensus introduce additional latency, particularly in resource-constrained HIoT environments. Existing studies mainly address system-level delay, while micro-latency from local processing, hashing, memory access, and verification remains comparatively less explored. This study reviews latency-reduction approaches across the data filtering and encryption layers using a micro–macro perspective. A dependency-aware model links local processing with propagation, queueing, consensus and storage. Reported results are interpreted according to their architectural and evaluation conditions, while component-level analysis distinguishes isolated effects from coupled architectural improvements, showing that end-to-end latency arises from interactions across multiple processing stages.

Keywords:

Blockchain,HIoT,Latency,Fog Computing,Micro and Macro latency.,

References:

I. A. A. Khan, A. A. Laghari, R. Alroobaea, A. M. Baqasah, M. Alsafyani, H. Alsufyani, and S. Ullah, “A lightweight scalable hybrid authentication framework for Internet of Medical Things (IoMT) using blockchain hyperledger consortium network with edge computing,” Scientific Reports, vol. 15, no. 1, p. 19856, 2025.
II. A. Ali, B. A. S. Al-Rimy, F. S. Alsubaei, A. A. Almazroi, and A. A. Almazroi, “HealthLock: Blockchain-based privacy preservation using homomorphic encryption in Internet of Things healthcare applications,” Sensors, vol. 23, no. 15, p. 6762, 2023.
III. A. Hashim, R. Rulaningtyas, and K. Chellappan, “Optimizing security and latency in blockchain-based health data management: A Hyperledger Fabric approach with multi-objective optimization,” Jurnal Kejuruteraan, vol. 37, no. 5, pp. 2469–2485, 2025.
IV. A. Shahidinejad and J. Abawajy, “An all-inclusive taxonomy and critical review of blockchain-assisted authentication and session key generation protocols for IoT,” ACM Computing Surveys, vol. 56, no. 7, pp. 1–38, 2024.
V. A. U. Rehman, N. Tariq, M. A. Jan, F. Khan, H. Song, and M. Ibrahim, “A blockchain-based hybrid model for IoMT-enabled intelligent healthcare system,” IEEE Transactions on Network Science and Engineering, vol. 11, no. 4, pp. 3512–3521, 2024.
VI. Bollineni, M. Sharma, A. Hazra, P. Kumari, S. Manipriya, and A. Tomar, “IoT for next-generation smart healthcare: A comprehensive survey,” IEEE Internet of Things Journal, vol. 12, no. 16, pp. 32616–32639, 2025.
VII. H. V. N. U. B. Murthy and M. Lawanya Shri, “An adaptive blockchain framework for federated IoMT with reinforcement learning-based consensus and resource forecasting,” Scientific Reports, 2026.
VIII. G. Niu, “A blockchain-based secure and privacy-preserving healthcare data management framework with SHA-256 and PoW consensus,” Informatica, vol. 49, no. 20, 2025.
IX. H. Gupta and P. Verma, “A survey on data security and latency frameworks in healthcare using blockchain,” in 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1745–1750, 2024.
X. H. Taherdoost, “Blockchain-based Internet of Medical Things,” Applied Sciences, vol. 13, no. 3, p. 1287, 2023.
XI. J. Babatope, “Designing energy-efficient cryptographic modules for securing real-time data communication in embedded Internet of Things (IoT) devices,” International Research Journal of Modernization in Engineering, Technology and Science, vol. 7, no. 8, p. 1995, 2025.
XII. J. E. Abang, H. Takruri, R. Al-Zaidi, and M. Al-Khalidi, “Latency performance modelling in Hyperledger Fabric blockchain: Challenges and directions with an IoT perspective,” Internet of Things, vol. 26, p. 101217, 2024.
XIII. K. Koshikawa, J.-D. Kim, W.-J. Hwang, K. Nguyen, H. Sekiya, “Dual Perigee: Reducing Latency in IoT Blockchain through Efficient Peer Selection”, Proc. IEEE 99th Vehicular Technology Conference (VTC2024-Spring), pp. 1–5, 2024.
XIV. K. Koshikawa, Y. Su, J.-D. Kim, W.-J. Hwang, Z. Li, K. Nguyen, and H. Sekiya, “Impacts of overlay topologies and peer selection on latencies in IoT blockchain,” IEEE Transactions on Network and Service Management, vol. 23, pp. 1630–1646, 2025.
XV. Anjum, N. Kraiem, H. Min, A. K. Dutta, Y. I. Daradkeh, and S. Shahab, “Opportunistic access control scheme for enhancing IoT-enabled healthcare security using blockchain and machine learning,” Scientific Reports, vol. 15, no. 1, p. 7589, 2025.
XVI. A. Obaidat, M. Rawashdeh, M. Alja’afreh, M. Abouali, K. Thakur, and A. Karime, “Exploring IoT and blockchain: A comprehensive survey on security, integration strategies, applications and future research directions,” Big Data and Cognitive Computing, vol. 8, no. 12, p. 174, 2024.
XVII. Ejaz, T. Kumar, I. Kovacevic, M. Ylianttila, and E. Harjula, “Health-BlockEdge: Blockchain-edge framework for reliable low-latency digital healthcare applications,” Sensors, vol. 21, no. 7, p. 2502, 2021.
XVIII. Kumar, A. Kumar, S. Verma, P. Bhattacharya, D. Ghimire, S.-H. Kim, and A. S. S. Hosen, “Healthcare Internet of Things (H-IoT): Current trends, future prospects, applications, challenges and security issues,” Electronics, vol. 12, no. 9, p. 2050, 2023.
XIX. Q. Al-Tuma and M. K. Ibrahem, “Enhanced secure blockchain-edge-Internet of Things system for real-time healthcare monitoring,” International Journal of Intelligent Engineering and Systems, vol. 18, no. 10, pp. 494–514, 2025.
XX. Zhou, L. Zeng, Y. Han, P. Li, F. Long, D. Zhou, I. Beschastnikh, and M. Wu, “Mercury: Fast transaction broadcast in high performance blockchain systems,” in IEEE INFOCOM 2023 – IEEE Conference on Computer Communications, pp. 1–10, 2023.
XXI. N. Kaur, A. Mittal, U. K. Lilhore, S. Simaiya, S. Dalal, K. Saleem, and E. S. Ghith, “Securing fog computing in healthcare with a zero-trust approach and blockchain,” EURASIP Journal on Wireless Communications and Networking, vol. 2025, no. 1, p. 5, 2025.
XXII. N. M. Ali, Y. A. El Ghafar, and G. A. El Khayat, “Reducing latency and enhancing data security in healthcare IoT systems using fog computing,” in 2025 International Conference on Machine Intelligence and Smart Innovation (ICMISI), pp. 7–12, 2025.
XXIII. N. S. Al-Blihed, N. F. Al-Mufadi, N. T. Al-Harbi, I. A. Al-Omari, and M. A. Al-Hagery, “Blockchain and machine learning in the Internet of Things: A review of smart healthcare,” IAES International Journal of Artificial Intelligence, vol. 12, no. 3, pp. 995–1006, 2023.
XXIV. O. Cheikhrouhou, K. Mershad, F. Jamil, R. Mahmud, A. Koubaa, and S. R. Moosavi, “A lightweight blockchain and fog-enabled secure remote patient monitoring system,” Internet of Things, vol. 22, p. 100691, 2023.
XXV. P. Karthikeyan and K. Brindha, “FogChainFlow: On-off blockchain data management for optimizing IoT healthcare,” Cluster Computing, vol. 28, no. 16, p. 1011, 2025.
XXVI. R. R. Pai and B. Pandey, “Edge computing for healthcare IoT: Designing distributed data processing architectures for clinical devices,” in Proc. IEEE Int. Conf. on Compute, Control, Network & Photonics (ICCCNP), pp. 1–6, 2025.
XXVII. S. Akter, M. Tabassum, R. Sultana, and M. S. H. Bhuiyan, “Blockchain for real-time healthcare data acquisition: A systematic review of sensor network applications and challenges,” American Journal of Interdisciplinary Studies, vol. 6, no. 1, pp. 208–235, 2025.
XXVIII. S. Badri, S. U. Jan, D. M. Alghazzawi, S. Aldhaheri, and N. Pitropakis, “BIoMT: A blockchain-enabled healthcare architecture for information security in the Internet of Medical Things,” Computer Systems Science and Engineering, vol. 46, no. 3, pp. 3667–3684, 2023.
XXIX. S. Guan, Y. Cao, and Y. Zhang, “Blockchain-enhanced data privacy preservation and secure sharing scheme for healthcare IoT,” IEEE Internet of Things Journal, vol. 12, no. 5, pp. 5600–5614, 2025.
XXX. S. Lee, M. Kim, J. Lee, R.-H. Hsu, M.-S. Kim, and T. Q. S. Quek, “Facing latency of Hyperledger Fabric for blockchain-enabled IoT: Modeling and analyses,” IEEE Network, vol. 37, no. 6, pp. 232–239, 2023.
XXXI. S. Muthuvel, S. Priya, and K. Sampath Kumar, “Design of a multi-layered privacy-preserving architecture for secure medical data exchange in cloud environments,” Scientific Reports, vol. 16, no. 1, 2026.
XXXII. S. M. and V. K. Chattu, “A review of artificial intelligence, big data, and blockchain technology applications in medicine and global health,” Big Data and Cognitive Computing, vol. 5, no. 3, p. 41, 2021.
XXXIII. S. Ravisankar and R. Maheswar, “SecureEdge-MedChain: A post-quantum blockchain and federated learning framework for real-time predictive diagnostics in IoMT,” Sensors, vol. 25, no. 19, p. 5988, 2025.
XXXIV. T. E. Ali, F. I. Ali, P. Dakić, and A. D. Zoltan, “Trends, prospects, challenges and security in the healthcare Internet of Things,” Computing, vol. 107, no. 1, p. 28, 2025.
XXXV. U. Islam, M. N. Alatawi, A. Alqazzaz, S. Alamro, B. Shah, and F. Moreira, “A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience,” Scientific Reports, vol. 15, no. 1, p. 25655, 2025.
XXXVI. W. V. Solis, J. M. Parra-Ullauri, and A. Kertesz, “Exploring the synergy of fog computing, blockchain and federated learning for IoT applications: A systematic literature review,” IEEE Access, vol. 12, pp. 68015–68060, 2024.
XXXVII. Y. Singh, M. A. Jabbar, S. K. Shandilya, O. Vovk, and Y. Hnatiuk, “Exploring applications of blockchain in healthcare: Road map and future directions,” Frontiers in Public Health, vol. 11, p. 1229386, 2023.
XXXVIII. Y. Zhu, C. Hua, D. Zhong, and W. Xu, “Design of low-latency overlay protocol for blockchain delivery networks,” IEEE Wireless Communications and Networking Conference (WCNC), pp. 1182–1187, 2022.

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Abstract:

Many traffic flow-based nonlocal models consist of microscopic Ordinary Differential Equations (ODEs) and macroscopic Partial Differential Equations (PDEs). In this paper, a novel mathematical model based on fractional and nonlocal partial differential equations (PDEs) is proposed to capture adaptive traffic flow dynamics in complex urban networks. Conventional models like the Lighthill–Whitham–Richards (LWR) model fail to capture interactions and memory impacts, relying on systems-based real traffic. To address these restrictions, a system-based fractional-order nonlocal PDE integrating the interactions-based spatial distribution and diffusion coefficients-based time-dependent adaptivity is proposed. The proposed model combines fractional derivatives for describing inherited traffic environmental conditions and nonlocal CORS for representing driver anticipation influences. Analytical properties consisting of steady state, presence, and solutions-based boundedness are considered over appropriate assumptions. A finite difference discretization of the numerical approach and an approximation method-based Grünwald–Letnikov scheme are modeled for simulation. MATLAB-based experiments illustrate that the proposed model captures a sharp change in pressure in a narrow region traveling through a medium, especially air, caused by an explosion or by a body moving faster than sound propagation, crowding formation, and scattering more precisely than conventional integer-order approaches. Six simulation scenarios demonstrate the fractional order effect and radius of nonlocal interaction of evolution-based traffic density. The key findings reflect that combining memory and nonlocality particularly enhances the capabilities of predictions and realism. The proposed framework offers a basis for modern traffic control schemes and opens novel trends for stratifying fractional PDEs in detailed dynamical systems.

Keywords:

partial differential equations,fractional calculus,traffic flow modeling,nonlocal dynamics,adaptive diffusion,

References:

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II. Ali Q, Al-Khaled K, Omar J, Raza A, Khan SU, Khan MI, Najati SA, Oreijah M, Guedri K, Galal AM., : ‘Analysis for advection–diffusion problem subject to memory effects and local and nonlocal kernels: A fractional operators approach’. International Journal of Modern Physics B. Vol.37)10:(2350099, 2023 Apr 20. 10.1142/S0217979223500996
III. Alotta G, Di Paola M, Pinnola FP., : ‘An unified formulation of strong non-local elasticity with fractional order calculus’. Meccanica. Vol.57,pp.793-805,2022. 10.1007/s11012-021-01428-x
IV. Bellido JC, Cueto J, Mora-Corral C., : ‘Non-local gradients in bounded domains motivated by continuum mechanics: Fundamental theorem of calculus and embeddings’. Advances in Nonlinear Analysis. Vol.12(1), 2023. 10.1515/anona-2022-0316
V. Ciaramaglia I, Goatin P., : ‘Moving bottlenecks in traffic flows: a well-posed fully coupled nonlocal PDE-ODE model with time delay’.INRIA.HAL.SCIENCE Web Portal . 2026.https://inria.hal.science/hal-05547499v1
VI. Demir T., :‘A structural stability framework for nonlocal fractional differential equations with memory-dependent kernels’.2026. 10.13140/RG.2.2.22103.53929
VII. ElGazzar A, van Gerven M., :‘Universal differential equations as a common modeling language for Neuroscience’.21 Mar2024. arXiv:2403.14510v1
VIII. JiaY,Wang D, GuoX., :‘High-dimensional numerical methods for nonlocal models’.Mathematics.Vol.13(21):3512,2025. 10.3390/math13213512
IX. Jones SR, Kuster GE., : ‘Examining students’ variational reasoning in differential equations’. The Journal of Mathematical Behavior.Vol. 64:100899, 2021. 10.1016/j.jmathb.2021.100899
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Abstract:

Structure, stability, and dissociation of HBeSiN, HOSiN, HSSiN, and their isomers HBeNSi, HONSi, and HSNSi have been studied in detail using density functional ωB97X-D, M062X, and ab initio MP2, CCSD and CCSD(T) methods. After dissociation of HBeNSi, HONSi, HSNSi, and their isomers, the fragmented atoms have been considered to be either in their ground state or in their valence excited state in various dissociation channels. Only allowed dissociations of these molecules are considered. Various dissociation channels of HBeNSi, HONSi, HSNSi and their isomers have been explored, and an interesting trend has been observed for the dissociation of stable isomers HBeNSi, HOSiN and HSNSi and less stable isomers HBeSiN, HONSi and HSSiN. The effects of substituents on their structural properties, PES for isomerization of RSiN RNSi along with their heat of formation, have been calculated using the G3 and G3B3LYP methods. The calculated results agree very well with the available theoretical values.

Keywords:

Structure; Stability; Isomerization; Dissociation; Density Functional and ab initio methods; RSiN; RNSi,

References:

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Abstract:

The aim of this paper is to introduce the new concept of the quadripartitioned Pythagorean Neutrosophic Super Hypersoft Set and discuss some of its properties. The focus of this paper is to propose a new notion of QPNSHS and study some basic operations and results in QPNSHS. Further, we develop a systematic study on QPNSHS and obtain various properties induced by them. Some equivalent characterizations and interrelations among them are discussed with counterexamples.

Keywords:

Quadri-partitioned Set,Neutrosophic Hypersoft Set,Super Hypersoft Set,

References:

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II. Chatterjee, Rajashi, et al. “On Some Similarity Measures and Entropy on Quadripartitioned Single Valued Neutrosophic Sets.” Journal of Intelligent & Fuzzy Systems, vol. 30, no. 4, Mar. 2016, pp. 2475–85. 10.3233/IFS-152017.
III. Maji, P. K., A. R. Roy, et al. “An Application of Soft Sets in a Decision Making Problem.” Computers & Mathematics with Applications, vol. 44, nos. 8–9, Oct. 2002, pp. 1077–83. 10.1016/S0898-1221(02)00216-X.
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Abstract:

Background: Visible light communication (VLC) based on red-green-blue (RGB) micro-light-emitting diode arrays can support high-speed indoor links while preserving the illumination function of the luminaire. However, the communication waveform may disturb the perceived colour point, the correlated colour temperature, the dimming level and the photobiological safety margin, so lighting quality cannot be treated as an external constraint. Objective: This study proposes a colorimetric-constrained probabilistic amplitude and colour-shift shaping (CC-PAS-CSK) framework for human-centric RGB micro-LED VLC, in which the information rate is maximized jointly with colour stability and blue-light safety. Methods: Unlike conventional colour-shift keying, which assigns symbols uniformly inside the RGB gamut, the proposed scheme selects nonuniform symbol probabilities and drive amplitudes so that the mean chromaticity remains near the target white point. A three-branch optical channel model, a CIE tristimulus transformation, a blue-light weighted exposure model and a filtered photodiode receiver are integrated into a single optimization problem, solved by an alternating colour-rate-safety algorithm. The alternation is formulated as block successive upper-bound minimization, for which monotonic improvement, subsequence convergence to a stationary point and a sufficient uniqueness condition are established. A second-order remainder bound is derived for the linearized chromaticity projection and used to tighten the linearized constraint, so that every iterate is feasible on the exact nonlinear CIE manifold. The temporal power spectral density of the shaped luminous waveform is derived in closed form and imposed as an additional convex constraint. Results: For a 5 m x 5 m indoor room, the proposed design reduces the chromaticity error by more than 50% compared with uniform colour-shift keying and improves the bit-error rate over clipped DCO-OFDM at the same lighting operating point. The algorithm converges within about 20 iterations and, at 20 dB average electrical signal-to-noise ratio, achieves a mean chromaticity error of 0.0019 with a blue-light safety margin of 0.96. Over 45 random initializations and five operating points, the objective is non-decreasing at every iteration, the measured contraction factor stays below 0.47, the first-order projection deviates from the exact projection by less than 0.3% of the colour tolerance, and the percentage modulation at 90 Hz falls from 2.4% to 0.6%, placing the design inside the IEEE 1789 low-risk region. Conclusions: Colour quality should be treated as a physical-layer resource in future VLC systems rather than as an external lighting constraint. Among the compared schemes, CC-PAS-CSK provides the best overall balance of data rate, chromaticity stability and photobiological safety.

Keywords:

Visible Light Communication,Micro-LED,Colour-Shift Keying,Probabilistic Shaping,Human-Centric Lighting,Blue-Light Safety,CIE Colorimetry,Block Successive Upper-Bound Minimization,Flicker Power Spectral Density,and Indoor Optical Wireless.,

References:

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Abstract:

This revised study develops a consistency-aware modified physics-informed neural network (PINN) formulation for fractional Bagley–Torvik differential–algebraic equations (fractional DAEs). The revision addresses the central mathematical issue raised by the review: a hereditary Caputo operator and an instantaneous algebraic constraint cannot be treated as unrelated penalty terms without a compatibility argument. The proposed formulation therefore introduces a coupled residual map, an implicit-function condition for the algebraic variable, and a reduced fractional operator obtained by eliminating the algebraic state locally. A consistency theorem is established showing that, under explicit Lipschitz, nonsingularity, approximation-density, and residual-stability assumptions, a sequence of network pairs whose composite residual tends to zero converges to the coupled solution manifold. The corresponding Volterra operator is shown to be continuous and compact under standard boundedness conditions, while a local contraction condition provides existence and uniqueness for the reduced initial-value formulation. The numerical material supplied with the original manuscript is retained and reanalyzed without introducing unreported computational values. For Example 1, the reported absolute-error data give MAE = 6.2454 × 10⁻³ and RMSE = 9.5014 × 10⁻³; for Example 2, the corresponding values are MAE = 1.1672 and RMSE = 2.0072. The revised discussion consequently distinguishes pointwise approximation quality from operator consistency and avoids unsupported claims of unconditional convergence. The resulting framework provides a mathematically controlled basis for applying modified PINNs to fractional Bagley–Torvik DAEs.

Keywords:

Bagley–Torvik equation; fractional differential-algebraic equations; Caputo derivative; physics-informed neural networks; operator consistency; convergence; Volterra operator.,

References:

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VIII. Bhatter, S., Nishant, Shyamsunder, Purohit, S. D., & Suthar, D. L. (2023). A study of incomplete I-functions relating to certain fractional integral operators. Applied Mathematics in Science and Engineering, 31(1), Article 2252996. 10.1080/27658449.2023.2252996
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XXI. Pomperada, J. R. (2022). Human resource information system with machine learning integration. Qubahan Academic Journal, 2(2), 5–8.
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XXIV. Salih, B. M., Abdulazeez, A. M., & Hassan, O. M. S. (2021). Gender classification based on iris recognition using artificial neural networks. Qubahan Academic Journal, 1(2), 156–163.
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XXVII. Saeed, S. R., Mohammed, A. H., Khaleel, S. K., & Al-Hilali, A. A. (2023). The study of deep learning for automotive logo recognition and classification. Periodicals of Engineering and Natural Sciences, 11(3), 255. 10.21533/pen.v11.i3.151

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Abstract:

The transient radiative magnetohydrodynamic bioconvective flow of an Fe₃O₄-MoS₂ hybrid nanofluid over a ramped, inclined, oscillating plate in a rotating porous medium is investigated numerically in this work. The model mathematically integrates the effects of heat and mass transfer, thermal radiation, rotation, porous permeability, and motile microbes. Using appropriate similarity transformations, governing equations for momentum, temperature, nanoparticle concentration, or microbe density are converted into the non-dimensional equations. The coupled system is then solved using the Crank-Nicolson finite-difference approach. The impact of several significant parameters, including Peclet number, magnetic parameter, bioconvection Lewis number, or radiation parameter, on the flow characteristics is investigated numerically. This establishes that fluid velocity increases as the Peclet number increases, but with higher bioconvection values. The Lewis number reduces the velocity profile. Temperature as well as concentration profiles Radiation and diffusion parameters significantly impact temperature and concentration profiles. by radiation and diffusion parameters.

Keywords:

Hybrid nanofluid; Bioconvection; porous medium; Thermal radiation; Oscillating plate; Crank Nicolson.,

References:

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II. Asaigeethan, P., Loganathan, K., Karthik, V., Priyadharshini, D., & Arunachalam, K. P. (2025). “Microbiologically Induced Bioconvection Over a Convectively Heated Rotating Frame: A Computational Model of the Blood-Based MHD Casson Hybrid Nanofluid Flow.” International Journal of Thermofluids, 101368.10.1016/j.ijft.2025.101368.
III. Almarashi, A. M., Algarni, A., Rooman, M., Shah, Z., & Garalleh, H. A. (2024). “Entropy generation and heat transfer analysis of unsteady micropolar magnetized hybrid-nanofluid flow over a radially stretchable permeable rotating disk with viscous and joule dissipation effects.” International Journal of Thermofluids, 23, 100802. 10.1016/j.ijft.2024.100802.
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XII. Choudhary, P., Choudhary, S., Jat, K., Loganathan, K., & Eswaramoorthi, S. (2024). “Significance of melting heat transfer and bioconvection phenomena in nanofluid flow over three different geometries.” International Journal of Thermofluids, 24, 100855.
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XVI. Lakshmikaanth, D., Selvaraj, A., Selvaraju, P., & Jose, S. D. (2023). “Hall and heat source effects of flow past a parabolic accelerated isothermal vertical plate in the presence of chemical reaction and radiation.” JP Journal of Heat and Mass Transfer, 34, 105-126.
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Abstract:

In recent years, researchers have become interested in human action recognition because of the broad spectrum of its applications in surveillance, healthcare monitoring, and human-computer interaction. A highly robust, efficient, and innovative multimodal system for recognizing human actions is proposed in this study based on the Florence 3D Action dataset. The proposed system leverages the RGB, Depth, and Skeleton multi-modalities. For the case of RGB and Depth video sequences, spatial information is extracted by a pre-trained ResNet50 model, and then the extracted high-level visual features are fed to a Temporal Convolutional Network (TCN) to capture temporal patterns at a large scale. Color and depth video sequences are processed in parallel. Temporal information is also captured in the skeleton data, which is fed to a Bi-directional Long Short-Term Memory (BiLSTM) model after features are extracted in the form of joint angles, velocities, and accelerations, which describe the motion of human actions. For the case of the three modalities, the results are fused at the decision level using a weighted sum for the final prediction. The results show that the proposed system has an impressive recognition accuracy of 96.8% on the Florence 3D dataset, with stable convergence, low validation loss and overfitting, and a strong generalization capability, which meets the requirements for use in real-time, resource-constrained systems.

Keywords:

HAR,ResNet50,Temporal Convolution,BiLSTM,Multimodal Fusion,RGBD,Florence 3d Dataset,

References:

I. Al-qaness, Mohammed AA, et al. "TCN-inception: temporal convolutional network and inception modules for sensor-based human activity recognition." Future Generation Computer Systems 160 (2024): 375-388. 10.1016/j.future.2024.06.016.
II. Anirudh, Rushil, et al. "Elastic functional coding of human actions: From vector-fields to latent variables." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. 10.1109/CVPR.2015.7298934.
III. Bai, Ruwen, et al. "Hierarchical graph convolutional skeleton transformer for action recognition." 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. 10.1109/ICME52920.2022.9859781.
IV. Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling." arXiv preprint arXiv:1803.01271 (2018). 10.48550/arXiv.1803.01271.
V. Batool, Mouazma, et al. "Multimodal human action recognition framework using an improved CNNGRU classifier." IEEE Access 12 (2024): 158388-158406. 10.1109/ACCESS.2024.3481631.
VI. Bijrothiya, Sadhna, and Vaibhav Soni. "An architecture for human activity recognition using TCN-BI-LSTM HAR based on wearable sensor." Procedia Computer Science 260 (2025): 805-813. 10.1016/j.procs.2025.03.261.
VII. Biswas, Sougatamoy, Anup Nandy, and Asim Kumar Naskar. "Lightweight multimodal feature fusion and spatiotemporal learning for human action recognition on edge devices." IEEE Transactions on Emerging Topics in Computational Intelligence (2025). 10.1109/TETCI.2025.3616054.
VIII. Chen, Junjie, et al. "A data augmentation method for skeleton-based action recognition with relative features." Applied Sciences 11.23 (2021): 11481. 10.3390/app112311481.
IX. Dhiman, Chhavi, Dinesh Kumar Vishwakarma, and Paras Agarwal. "Part-wise spatio-temporal attention driven CNN-based 3D human action recognition." ACM Transactions on Multimidia Computing Communications and Applications 17.3 (2021): 1-24. 10.1145/3441628.
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XII. Hidayanto, Nur Awal, Adhi Prahara, and Riky Dwi Puriyanto. "Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor." Jurnal Informatika Ahmad Dahlan 15.1 (2021): 17-27. 10.26555/jifo.v15i1.a20106.
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XXII. Rana, Vijay Singh, Ankush Joshi, and Kamal Kant Verma. "A Unified Deep Learning Approach for Human Activity Recognition with RGB-D Data." 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS). IEEE, 2025. 10.1109/IETACS68750.2025.11385548.
XXIII. Sanchez-Caballero, Adrian, David Fuentes-Jimenez, and Cristina Losada-Gutiérrez. "Real-time human action recognition using raw depth video-based recurrent neural networks." Multimedia Tools and Applications 82.11 (2023): 16213-16235. 10.1109/ACCESS.2024.3481631.
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XXIX. Verma, Kamal Kant, and Brij Mohan Singh. "Deep Multi-Model Fusion for Human Activity Recognition Using Evolutionary Algorithms." International Journal of Interactive Multimedia and Artificial Intelligence 7.2 (2021): 44-58. 10.9781/ijimai.2021.08.008.
XXX. Verma, Kamal Kant, Brij Mohan Singh, and Amit Dixit. "A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system." International Journal of Information Technology 14.1 (2022): 397-410. 10.1007/s41870-019-00364-0.
XXXI. Verma, Kamal Kant, et al. "Two-stage human activity recognition using 2D-ConvNet." IJIMAI 6.2 (2020): 125-135. 10.9781/ijimai.2020.04.002.
XXXII. Vijay Singh Rana, Ankush Joshi, Kamal Kant Verma, "Lightweight 3DCNN-BiLSTM Model for Human Activity Recognition using Fusion of RGBD Video Sequences", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.6, pp.176-193. 2025. 10.5815/ijitcs.2025.06.10
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XXXVIII. Zhang, Yumin, and Yanyong Wang. "A comprehensive survey on RGB-D-based human action recognition: algorithms, datasets, and popular applications." EURASIP Journal on Image and Video Processing 2025.1 (2025): 15. 10.1186/s13640-025-00677-0.

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Abstract:

Mobile Ad hoc Networks (MANETs) are decentralized, infrastructure-less networks characterized by dynamic topology, making them highly vulnerable to security threats such as blackhole attacks and prone to performance degradation under increasing network scale. Most established routing protocols cannot simultaneously address adaptive routing efficiency and intrusion detection with low computational overhead. An enhanced routing framework, Adaptive Hybrid Antnet with Optimized Support Vector Machine (AH-MantnetOpSVM), presented in this paper, offers adaptive, scalable, and secure communication in MANETs. This approach combines adaptive parameter tuning into the Antnet routing mechanism for the dynamic regulation of pheromone updates based on the residual energy of the node and real-time congestion levels while incorporating an optimized SVM-based intrusion detection for identifying and isolating malicious nodes involved in blackhole attacks, with reduced computational overhead. There is a consistent and substantial improvement in packet delivery ratio, throughput, and network lifetime while reducing delay and false positive rate. Unlike conventional approaches that suffer performance degradation as node density increases, AH-MantnetOpSVM consistently maintains stable and reliable performance across varying network scales, thereby effectively enhancing both security and scalability in the MANET environment.

Keywords:

Mobile Ad Hoc Networks,Ant Colony Optimization,Support Vector Machine,Intrusion Detection System,Blackhole Attack,Secure Routing.,

References:

I. Abbood, Z. A., D. C. Atilla, and C. Aydin. “Intrusion Detection System through Deep Learning in Routing MANET Networks.” Intelligent Automation & Soft Computing. 2023;37(1). 10.32604/iasc.2023.035276.
II. Abdallah, A. A., M. S. Abdallah, H. Aslan, M. A. A. Abdallah, Y. I. Cho, and M. S.Abdallah. “Enhancing Mobile Ad Hoc Network Security: An Anomaly Detection Approach Using Support Vector Machine for Black-Hole Attack Detection.” International Journal of Safety & Security Engineering. 2024;14(4). 10.18280/ijsse.140401.
III. Abdan, M., and S. A. H. Seno. “Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET).” Wireless Communications and Mobile Computing. 2022;2022(1):2375702. 10.1155/2022/2375702.
IV. Abdullah, A. M., E. Ozen, and H. Bayramoglu. “Energy Efficient MANET Routing Protocol Based on Ant Colony Optimization.” Adhoc & Sensor Wireless Networks. 2020;47.
V. Adanigboa, O. O., S. Aina, A. I. Oluwaranti, and S. O. Oseni. “Advances in Energy Efficiency and Routing Optimization in Mobile Ad Hoc Networks: A Comprehensive Literature Review.” Journal of Engineering, Technology, and Innovation. 2025;4(1):1–7.
VI. Almuhanna, R., and S. Dardouri. “A Deep Learning/Machine Learning Approach for Anomaly-Based Network Intrusion Detection.” Frontiers in Artificial Intelligence. 2025;8:1625891. 10.3389/frai.2025.1625891.
VII. Anantapur, M., and V. C. Patil. “Ant Colony Optimization Based Modified AODV for Secure Routing in Mobile Ad Hoc Networks.” International Journal of Intelligent Engineering and Systems. 2021;14(6):115–124. 10.22266/ijies2021.1231.11.
VIII. Arthi, A., A. Beno, S. Sharma, and B. Sangeetha. “Fuzzy Based Inference System with Ensemble Classification Based Intrusion Detection System in MANET.” Journal of Intelligent & Fuzzy Systems. 2023;45(3):3567–3574. 10.3233/JIFS-230161.
IX. da Costa Bento, C. R., and E. C. G. Wille. “Bio-Inspired Routing Algorithm for MANETs Based on Fungi Networks.” Ad Hoc Networks. 2020;107:102248. 10.1016/j.adhoc.2020.102248.
X. Dash, Y. “Nature Inspired Routing in Mobile Ad Hoc Network.” 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). 2021:1299–1303. 10.1109/ICAC3N53548.2021.9725487.
XI. Gopalasamy, K., and K. G. Muthaiya. “Optimizing Packet Routing and Security in MANETs with the H-MAntnetSVM Algorithm for Energy Efficiency and Blackhole Detection.” Sustainable Computing: Informatics and Systems. 2025;46:101123. 10.1016/j.suscom.2025.101123.
XII. Hande, J. Y., and R. Sadiwala. “Optimization of Energy Consumption and Routing in MANET Using Artificial Neural Network.” Journal of Integrated Science and Technology. 2024;12(1):718–718. 10.62110/sc5m8x71.
XIII. Hemalatha, S., K. T. Reddy, T. Ramaswamy, and R. V. V. Krishna. “Enhancing MANET Security Using AI-Driven Intrusion Detection Systems.” Journal of Communications. 2025;20(4):257–264. 10.12720/jcm.20.4.257-264.
XIV. G. Kalaiselvi and M. G. Kavitha. “Energy and Security Aware Routing in MANETs: A Hybrid Antnet with Optimized SVM Based Blackhole Attack Detection (H-MantnetOpSVM).” 2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN). 2025:1–6. 10.1109/RFCoN65425.2025.11002026.
XV. Khan, D. M., T. Aslam, N. Akhtar, S. Qadri, I. M. Rabbani, and M. Aslam. “Black Hole Attack Prevention in Mobile Ad-Hoc Network (MANET) Using Ant Colony Optimization Technique.” Information Technology and Control. 2020;49(3):308–319. 10.5755/j01.itc.49.3.25288.
XVI. Khanpara, P., and S. Valiveti. “An Efficient Bio-Inspired Routing Scheme for Tactical Ad Hoc Networks.” Scalable Computing: Practice and Experience. 2023;24(1):45–54. 10.12694/scpe.v24i1.2186.
XVII. Li, W., L. Xia, Y. Huang, and S. Mahmoodi. “An Ant Colony Optimization Algorithm with Adaptive Greedy Strategy to Optimize Path Problems.” Journal of Ambient Intelligence and Humanized Computing. 2022;13(3):1557–1571. 10.1007/s12652-020-02531-1.
XVIII. Malyadri, N., R. M., R. Nandalike, P. Chavan, S. S., D. P., and R. S. “A Predictive Energy‐Efficient Adaptive Routing Methodology for Mobile Ad Hoc Networks.” IET Networks. 2025;14(1):e70001. 10.1049/ntw2.70001.
XIX. Nirmala Bai, K. S., and D. M. Subramanyam. “Integrated Intrusion Detection Design with Discretion of Leading Agent Using Machine Learning for Efficient MANET System.” Scientific Reports. 2025;15(1):30849. 10.1038/s41598-025-14923-7.
XX. Prasad, M., S. Tripathi, and K. Dahal. “An Intelligent Intrusion Detection and Performance Reliability Evaluation Mechanism in Mobile Ad-Hoc Networks.” Engineering Applications of Artificial Intelligence. 2023;119:105760. 10.1016/j.engappai.2022.105760.
XXI. Prasanna, K. S., and B. Ramesh. “A Review: An Efficient and Reliable Secure Routing Mechanism with the Prevention of Attacks in Mobile Ad-Hoc Network (MANET).” Wireless Personal Communications. 2023;133(4):2541–2580. 10.1007/s11277-023-10556-0.
XXII. Rahman, M. T., M. Alauddin, U. K. Dey, and S. Sadi. “Adaptive, Secure and Efficient Routing Protocol to Enhance the Performance of Mobile Ad Hoc Network (MANET).” Applied Computer Science. 2023;19(3). 10.23743/acs-2023-21.
XXIII. Sardar, T. H., S. A. Dar, J. Jaiswal, G. Prasad, M. Mittal, and V. Kumar. “Enhancing Security in MANETs with Deep Learning-Based Intrusion Detection.” Procedia Computer Science. 2025;259:120–129. 10.1016/j.procs.2025.01.316.
XXIV. Venketesh, R., and K. Sasikala. “The Design of an Intrusion Detection System in MANET Using the IGWO-ANN Classification Algorithm.” International Journal of Networking and Virtual Organisations. 2024;31(1):22–42. 10.1504/IJNVO.2024.136007.
XXV. Wu, L., X. Huang, J. Cui, C. Liu, and W. Xiao. “Modified Adaptive Ant Colony Optimization Algorithm and Its Application for Solving Path Planning of Mobile Robot.” Expert Systems with Applications. 2023;215:119410 . 10.1016/j.eswa.2022.119410.
XXVI. Yilmaz, K., R. Kara, and F. Katircioglu. “Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETs.” IEEE Access. 2025. 10.1109/ACCESS.2025.3551234.

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Abstract:

Speaker identification is an important task in speech processing, with applications in security, authentication, forensics, and personalised human–computer interaction. This study presents a lightweight convolutional neural network (CNN)-based system for close-set identification of 40 speakers using Mel-Frequency Cepstral Coefficients (MFCCs). A dataset comprising 1,000 speech samples was collected from 40 speakers, with 850 samples used for training and 150 for validation. MFCC features were extracted from the speech signals and used as input to a compact CNN consisting of four convolutional blocks, three intermediate pooling operations, adaptive average pooling, and a fully connected classification layer. The proposed architecture contains 102,792 trainable parameters. Under the evaluated controlled conditions, the model achieved 100% validation accuracy, with all 150 validation samples correctly classified, and achieved an ROC-AUC of 1.00 for all 40 speaker classes. Pooling ablation experiments showed that removing any one of the three intermediate pooling operations reduced the best validation accuracy from 100.00% to 99.33%, supporting the use of the complete pooling configuration. An augmentation sensitivity analysis further showed that the unaugmented configuration achieved the highest validation accuracy of 100.00%, while pitch-shift augmentation resulted in 99.00% and Gaussian-noise augmentation reduced accuracy to 2.67%, both independently and when combined with pitch shifting. These findings indicate that the unaugmented configuration was the most effective setting for the proposed model under the evaluated conditions. Overall, the proposed lightweight CNN demonstrates effective closed-set speaker identification under controlled recording conditions, while further evaluation is required for open-set recognition, speaker verification, and greater recording variability.

Keywords:

MFCC,CNN,Classification,Confusion matrix,Speaker Identification,

References:

I. Brümmer, Niko, and Johan de Villiers. “The BOSARIS Toolkit and Calibration Methods for Speaker Recognition Scoring.” Proceedings of the 2011 NIST Speaker and Language Recognition Workshop, 2011.
II. B. Tan, M. H. A. Hijazi, N. Khamis, P. N. E. binti Nohuddin, Z. Zainol, F. Coenen, and A. Gani, “A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction,” Multimedia Tools and Applications, vol. 80, nos. 21–23, pp. 32725–32762, 2021. doi: 10.1007/s11042-021-11235-x
III. Chakraborty, Koustav, Asmita Talele, and Savitha Upadhya. “Voice Recognition Using MFCC Algorithm.” International Journal of Innovative Research in Advanced Engineering, vol. 1, no. 10, 2014, pp. 158–161.
IV. Chung, Joon Son, Arsha Nagrani, and Andrew Zisserman. “VoxCeleb2: Deep Speaker Recognition.” Interspeech 2018, 2018, pp. 1086–1090. 10.21437/Interspeech.2018-1929.
V. Dehak, Najim, Patrick Kenny, Réda Dehak, Pierre Dumouchel, and Pierre Ouellet. “Front-End Factor Analysis for Speaker Verification.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 4, 2011, pp. 788–798. 10.1109/TASL.2010.2064307.
VI. Desplanques, Brecht, Jenthe Thienpondt, and Kris Demuynck. “ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification.” Interspeech 2020, 2020, pp. 3830–3834. 10.21437/Interspeech.2020-2650.
VII. Desplanques, Brecht, Jenthe Thienpondt, and Kris Demuynck. “Large Margin Fine-Tuning for Speaker Recognition.” 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020. 10.1109/ICASSP40776.2020.9054465.
VIII. Ferrer, Luciana, Matthew McLaren, and Niko Brümmer. “Robust Back-End Calibration and Adaptation Techniques for Speaker Recognition.” Computer Speech & Language.
IX. Garcia-Romero, Daniel, and Carol Y. Espy-Wilson. “Analysis of i-Vector Length Normalization in Speaker Recognition Systems.” Interspeech 2011, 2011, pp. 249–252. 10.21437/Interspeech.2011-53.
X. Garcia-Romero, Daniel, and Aaron McCree. “Multicondition Training of Gaussian PLDA Models in i-Vector Space for Noise and Reverberation Robust Speaker Recognition.” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 4257–4260. 10.1109/ICASSP.2012.6288859.
XI. Garcia-Romero, Daniel, et al. “The UMD-JHU 2011 Speaker Recognition System.” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 4225–4228. 10.1109/ICASSP.2012.6288852.
XII. He, Y., et al. “Exploration of Data Augmentation and Augmentation Strategies for Speaker Recognition.” Conference paper.
XIII. Heigold, Georg, Ignacio Moreno, Samy Bengio, and Noam Shazeer. “End-to-End Text-Dependent Speaker Verification.” 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 5115–5119. 10.1109/ICASSP.2016.7472652.
XIV. Ioffe, Sergey. “Probabilistic Linear Discriminant Analysis.” Computer Vision—ECCV 2006 Workshops, Springer, 2006, pp. 531–542. 10.1007/11744085_41.
XV. Ji, R., et al. “An End-to-End Text-Independent Speaker Identification Framework.” Interspeech 2018, 2018, pp. 3267–3271.
XVI. Kenny, Patrick. “Joint Factor Analysis of Speaker and Session Variability: Theory and Algorithms.” Technical Report CRIM-06/08-13, 2005.
XVII. Khan, Suhail Ahmad, Anil S. Thosar, Jagannath H. Nirmal, and Vinay S. Pande. “A Unique Approach in Text Independent Speaker Recognition Using MFCC Feature Sets and Probabilistic Neural Network.” 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), IEEE, 2015, pp. 1–6.
XVIII. Kinnunen, Tomi, and Haizhou Li. “An Overview of Text-Independent Speaker Recognition: From Features to Supervectors.” Speech Communication, vol. 52, no. 1, 2010, pp. 12–40. 10.1016/j.specom.2009.08.009.
XIX. Lee, Kong Aik, et al. “Data-Efficient Speaker Recognition and Few-Shot Adaptation Techniques.” Conference paper.
XX. Lei, Yun, et al. “A Novel Scheme for Speaker Recognition Using a PLDA Variant.” 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 1695–1699.
XXI. Nagrani, Arsha, Joon Son Chung, and Andrew Zisserman. “VoxCeleb: A Large-Scale Speaker Identification Dataset.” Interspeech 2017, 2017, pp. 2616–2620. 10.21437/Interspeech.2017-950.
XXII. Okabe, Koji, Takafumi Koshinaka, and Koichi Shinoda. “Attentive Statistics Pooling for Deep Speaker Embedding.” Interspeech 2018, 2018, pp. 2252–2256. 10.21437/INTERSPEECH.2018-993.
XXIII. Okada, H., et al. “Improvements in i-Vector PLDA Scoring and Calibration for NIST SREs.” IEEE workshop/conference paper.
XXIV. Povey, Daniel, et al. “The Kaldi Speech Recognition Toolkit.” 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, 2011.
XXV. Ravanelli, Mirco, and Yoshua Bengio. “Speaker Recognition from Raw Waveform with SincNet.” Interspeech 2018, 2018, pp. 3698–3702. 10.21437/Interspeech.2018-2492.
XXVI. Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. “Speaker Verification Using Adapted Gaussian Mixture Models.” Digital Signal Processing, vol. 10, nos. 1–3, 2000, pp. 19–41. 10.1006/dspr.1999.0362.
XXVII. Sigona, Francesco, et al. “Validation of an ECAPA-TDNN System for Forensic Automatic Speaker Recognition.” Speech Communication, 2024. 10.1016/j.specom.2024.103045.
XXVIII. Snyder, David, et al. “Spoken Language Recognition Using X-Vectors.” Odyssey 2018: The Speaker and Language Recognition Workshop, 2018, pp. 105–111. 10.21437/Odyssey.2018-15.
XXIX. Snyder, David, Daniel Garcia-Romero, Gregory Sell, Daniel Povey, and Sanjeev Khudanpur. “X-Vectors: Robust DNN Embeddings for Speaker Recognition.” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 5329–5333. 10.1109/ICASSP.2018.8461375.
XXX. Sztahó, Dániel, Gábor Szaszák, and Anna Beke. “Deep Learning Methods in Speaker Recognition: A Review.” Periodica Polytechnica Electrical Engineering and Computer Science, vol. 65, no. 4, 2021, pp. 310–328. 10.3311/PPee.17024.
XXXI. Variani, Ehsan, Xin Lei, Erik McDermott, Ignacio Lopez Moreno, and Javier Gonzalez-Dominguez. “Deep Neural Networks for Small-Footprint Text-Dependent Speaker Verification.” 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 4052–4056. 10.1109/ICASSP.2014.6854363.
XXXII. Villalba, Jesús, and Niko Brümmer. “Towards Fully Bayesian Speaker Recognition: Integrating Out the Between-Speaker Covariance.” Interspeech 2011, 2011, pp. 505–508. 10.21437/Interspeech.2011-142.
XXXIII. Villalba, Jesús, et al. “Domain Adaptation Techniques for PLDA and Neural Back-Ends.” ICASSP/Interspeech proceedings.
XXXIV. Wan, Li, Quan Wang, Alan Papir, and Ignacio Lopez Moreno. “Generalized End-to-End Loss for Speaker Verification.” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 4879–4883. 10.1109/ICASSP.2018.8462665.
XXXV. Wu, Zhizheng, Tomi Kinnunen, Nicholas Evans, Junichi Yamagishi, Cemal Hanilçi, Md. Sahidullah, and Aleksandr Sizov. “ASVspoof 2015: The First Automatic Speaker Verification Spoofing and Countermeasures Challenge.” Interspeech 2015, 2015, pp. 2037–2041. 10.21437/Interspeech.2015-462.
XXXVI. Wu, Zhizheng, et al. “ASVspoof 2017/2019 Challenge Series: Challenge Overview and Datasets.” Interspeech Proceedings.

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Abstract:

Pressure from customers and stakeholders has forced manufacturing firms to adopt sustainable manufacturing practices into their operations and supply chain management strategies. The three core principles should be considered during their implementation, that consists of economic, environmental, and social. The selection and prioritization of these three core principles become a complex decision-making process. The multi-criteria decision-making techniques can serve as a medium to ease the process of selection and prioritization in a complex system. Thus, this study focuses on prioritizing the sustainable manufacturing practices by determining the weights through Fuzzy Analytic Hierarchy Process in the Malaysian manufacturing sector. The practices were identified through a literature review, questionnaire survey, and consensus obtained from a panel of experts. The findings indicate that environmental was identified as the most important criterion, followed by economic and social. The environmental aspect was emphasized by two: the Environmental Conservation Program and Environmental Recycling and Prevention Program. This finding reveals that the Malaysian manufacturing sector is focusing on ‘green factories’ since this sector consumes a high percentage of final energy use. In addition, this finding will be beneficial to manufacturing practitioners who intend to transform from conventional to sustainable one. The manufacturing practitioner will be facilitated to emphasize the important practice and develop a strategy plan to adopt sustainable manufacturing practices.

Keywords:

Sustainable Manufacturing Practice,Malaysian Manufacturing Firm,Multi-Criteria Decision Making,Fuzzy Analytic Hierarchy Process,

References:

I. A. Bagherian, C. Laux and M. Kondala. : ‘Transforming quality management: A fuzzy AHP-based approach to Quality 5.0 decision-making’. Quality Management Journal. vol. 33, no. 2, pp. 83-105. 2026. 10.1080/10686967.2025.2579156
II. A. Kumar, S. Luthra, S. K. Mangla and Y. Kazançoğlu. : ‘COVID-19 impact on sustainable production and operations management’. Sustainable Operation and Computer. vol. 1, pp. 1–7. 2020. 10.1016/j.susoc.2020.06.001
III. Suwarna, I. Khasanah and A. Choerudin. : Sustainable operations management: evaluating green supply chain practices and their effect on competitive advantage’. Journal Management & Economics Review (JUMPER). vol. 3, no. 8, pp. 465-478. 2026. 10.59971/jumper.v3i8.863
IV. K. B. Bour, A.J. Asafo, and B.O. Kwarteng. : ‘Study on the effects of sustainability practices on the growth of manufacturing companies in urban Ghana’. Heliyon, vol. 5, no. 6, 2019. 10.1016/j.heliyon.2019.e01903
V. Huang, H. Liu, E. Oghenerobor and C. S. Chen. : ‘Collaborating digitalization and green supply chain to promote green development of manufacturing firms–an analysis from a configurational perspective’. Chinese Management Studies. vol. 20, no. 8, pp. 2133-2155. 2026. 10.1108/CMS-08-2024-0570
VI. Etemad, S. Nazari-Shirkouhi, S. K. Chaharsooghi and K. Govindan. : ‘Relationship between lean, agile, resilient and green paradigm and sustainable supply chain performance: an empirical investigation’. Industrial Management & Data Systems. pp. 1-36. 2026. 10.1108/IMDS-05-2025-0725
VII. Farrukh Shahzad, H. Liu and H. Zahid. : ‘Industry 4.0 technologies and sustainable performance: do green supply chain collaboration, circular economy practices, technological readiness and environmental dynamism matter?’ Journal of Manufacturing Technology Management. vol. 36, no. 1, pp. 1-22. 2025. 10.1108/JMTM-05-2024-0236
VIII. J. Cohen. : ‘Does the COVID-19 outbreak mark the onset of a sustainable consumption transition?’ Sustainability: Science, Practice and Policy. vol. 16, no. 1, pp. 1–3. 2020. 10.1080/15487733.2020.1740472
IX. Z. Yusup, W. H. W. Mahmood, M. R. Salleh and M. R. Muhamad. : ‘The influence factor for the successful implementation of cleaner production: a review’. Jurnal Teknologi (Sciences & Engineering), vol. 67, no. 1, 2014. 10.11113/jt.v67.2160
X. N. R. M. Alias, N. Hami and S.M. Shafie. : ‘A case study analysis of sustainable manufacturing practice in Malaysian manufacturing firm’. Journal of Technology and Operations Management. vol. 13, no. 1, pp. 68-77. 2018. 10.32890/jtom2018.13.1.7
XI. S. J. H. Dehshiri. : ‘Sustainable supplier selection based on a comparative decision-making approach under uncertainty’. Spectrum of Operational Research. vol. 3, no. 1, pp. 238-251. 2026. 10.31181/sor31202644
XII. S. Luthra, A. Kumar, E. K. Zavadskas, S. K. Mangla and J. A. Garza-Reyes. : ‘Industry 4.0 as an enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers in an emerging economy’. International Journal of Production Research. vol. 58, no. 5, pp. 1505–1521. 2019. 10.1080/00207543.2019.1660828
XIII. S. Zighan, Z. Alkalha and L. Jum’a. : ‘Barriers to circular economy transitions in emerging markets: insights from the GCC and implications for sustainable development.’ Sustainable Development. vol. 34, pp. 210-225. 2026. 10.1002/sd.70177
XIV. S. H. Abdul-Rashid, N. Sakundarini, R.A.R. Ghazilla and R. Thurasamy. : ‘The impact of sustainable manufacturing practices on sustainability performance: Empirical evidence from Malaysia’. International Journal of Operations & Production Management. vol. 37, no. 2, pp. 182-204. 2017. 10.1108/IJOPM-04-2015-0223
XV. W. H. W. Mahmood, M. N. Ab Rahman and B. M. Deros. : ‘Green supply chain management in Malaysian aero composite industry’. Jurnal Teknologi (Sciences and Engineering), vol. 59, pp. 13-17. 2012.

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Abstract:

Bone quantitative ultrasound (QUS) relies on the dispersion of guided waves propagating in the cortical shell of long bones. Analytical fluid–solid–fluid trilayer models obtain the dispersion relation from a characteristic determinant whose roots must be located numerically, a procedure that becomes delicate near mode osculations and for anisotropic cortical bone. Here the fluid–solid–fluid waveguide is reformulated as a linear generalized eigenvalue problem using a Chebyshev spectral-collocation method. Each layer is discretized on a Chebyshev–Gauss–Lobatto grid, and the interface and free-surface conditions are imposed directly by row replacement. For a prescribed wavenumber, the eigenvalue is W = ω², so every finite discrete eigenmode follows from a single generalized eigensolve without determinant root searching; the physical guided branches are then selected by explicit admissibility and participation criteria. The method is validated against an independently implemented analytical global-matrix solver for a water/aluminum/water trilayer: the fundamental extensional (S₀) and flexural (A₀) phase velocities agree within numerical tolerance (better than 10⁻⁵ %), and a convergence study with eigen-residual norms confirms spectral accuracy at about fourteen collocation nodes per layer. The same framework is then extended to a transversely isotropic cortical-bone layer and, with one additional volume-fraction field, to a linear elastic material with voids. Transverse isotropy raises the low-frequency extensional plateau by about 8.2 %, whereas increasing void coupling lowers it by up to 15 %. A Hellmann–Feynman sensitivity analysis shows that this plateau constrains only one stiffness combination, so these figures are forward-model discrepancies rather than uniquely recoverable inverse biases.

Keywords:

guided waves; cortical bone; quantitative ultrasound; spectral collocation; transverse isotropy; elastic materials with voids,

References:

I. J. D. Achenbach, Wave Propagation in Elastic Solids, North-Holland, Amsterdam (1973).
II. A. T. I. Adamou, R. V. Craster, Spectral methods for modelling guided waves in elastic media, Journal of the Acoustical Society of America 116(3) (2004) 1524–1535. 10.1121/1.1777871
III. U. Andreaus, I. Giorgio, A. Madeo, Modeling of the interaction between bone tissue and resorbable biomaterial as linear elastic materials with voids, Zeitschrift für angewandte Mathematik und Physik (ZAMP) 66(1) (2015) 209–237. 10.1007/s00033-014-0403-z
IV. N. Bochud, Q. Vallet, Y. Bala, H. Follet, J.-G. Minonzio, P. Laugier, Genetic algorithms-based inversion of multimode guided waves for cortical bone characterization, Physics in Medicine and Biology 61(19) (2016) 6953–6974. 10.1088/0031-9155/61/19/6953
V. A. Chaboty, V.-H. Nguyen, G. Haïat, P. Bélanger, Cortical bone plate properties assessment using inversion of axially transmitted low-frequency ultrasonic guided waves, Journal of the Acoustical Society of America 156(2) (2024) 954–967. 10.1121/10.0028173
VI. S. C. Cowin, J. W. Nunziato, Linear elastic materials with voids, Journal of Elasticity 13(2) (1983) 125–147. 10.1007/BF00041230
VII. M. A. Denolle, E. M. Dunham, G. C. Beroza, Solving the surface-wave eigenproblem with Chebyshev spectral collocation, Bulletin of the Seismological Society of America 102(3) (2012) 1214–1223. 10.1785/0120110183
VIII. M. A. Goodman, S. C. Cowin, A continuum theory for granular materials, Archive for Rational Mechanics and Analysis 44(4) (1972) 249–266. 10.1007/BF00284326
IX. M. Granke, Q. Grimal, A. Saïed, P. Nauleau, F. Peyrin, P. Laugier, Change in porosity is the major determinant of the variation of cortical bone elasticity at the millimeter scale in aged women, Bone 49(5) (2011) 1020–1026. 10.1016/j.bone.2011.08.002
X. F. Hernando Quintanilla, Z. Fan, M. J. S. Lowe, R. V. Craster, Guided waves’ dispersion curves in anisotropic viscoelastic single- and multi-layered media, Proceedings of the Royal Society A 471(2183) (2015) 20150268. 10.1098/rspa.2015.0268
XI. D. A. Kiefer, G. Watzl, K. Burgholzer, M. Ryzy, C. Grünsteidl, Electroelastic guided wave dispersion in piezoelectric plates: spectral methods and laser-ultrasound experiments, Journal of Applied Physics 137(11) (2025) 114502. 10.1063/5.0250494
XII. P. Laugier, G. Haïat (eds.), Bone Quantitative Ultrasound, Springer (2010). 10.1007/978-94-007-0017-8
XIII. L. H. Le, V.-H. Nguyen, T. N. H. T. Tran, Ultrasonic guided waves in bone: a decade of advancement in review, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69(9) (2022) 2540–2557.
XIV. J.-G. Minonzio, N. Bochud, Q. Vallet, Y. Bala, D. Ramiandrisoa, H. Follet, D. Mitton, P. Laugier, Bone cortical thickness and porosity assessment using ultrasound guided waves: an ex vivo validation study, Bone 116 (2018) 111–119. 10.1016/j.bone.2018.07.018
XV. H. Nguyen, D. Le, E. Plan, S. T. Dang, H. Phan, Theoretical model of guided waves in a bone-mimicking plate coupled with soft-tissue layers, Vietnam Journal of Mechanics 43(1) (2021) 91–104. 10.15625/0866-7136/15774
XVI. V.-H. Nguyen, T. N. H. T. Tran, M. D. Sacchi, S. Naili, L. H. Le, Computing dispersion curves of elastic/viscoelastic transversely-isotropic bone plates coupled with soft tissue and marrow using a semi-analytical finite element (SAFE) method, Computers in Biology and Medicine 87 (2017) 371–381. 10.1016/j.compbiomed.2017.06.001
XVII. J. W. Nunziato, S. C. Cowin, A nonlinear theory of elastic materials with voids, Archive for Rational Mechanics and Analysis 72(2) (1979) 175–201. 10.1007/BF00249363
XVIII. J. L. Rose, Ultrasonic Guided Waves in Solid Media, Cambridge University Press (2014).
XIX. L. N. Trefethen, Spectral Methods in MATLAB, SIAM, Philadelphia (2000). 10.1137/1.9780898719598
XX. J. A. C. Weideman, S. C. Reddy, A MATLAB differentiation matrix suite, ACM Transactions on Mathematical Software 26(4) (2000) 465–519. 10.1145/365723.365727

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Abstract:

With the rapid increase of the societal requirements and the complexity of the industrial systems there is a great need to achieve high reliability without losing profitability. Small system failures might lead to huge loss of operational activities, downtimes and low productivity. In this paper, the authors propose a stochastic reliability model to analyze the availability and profitability of an industrial system under environmental factors and fluctuating demand. The proposed model assumes that it is a single unit system where there are no failures that can be tolerated. It is also equipped with an inspection mechanism to identify the kind of failure occurring in the system. The failures are called minor and major failures.Minor failures are caused by lubrication or greasing problems which can be repaired and major failures are caused by electrical short circuiting, water ingestion in the time of adverse weather and overloading as well as prolonged continuous operation . In case of major failures the system is either repaired or replaced depending upon the extent of damage. The two operating cases are studied where the demand is higher than or equal to the production and the demand is lower than the production. The regenerative point technique and Semi-Markov processes are used to evaluate key performance measures and profit thoroughly with respect to the effects of different parameters of the system.Data has been collected from a steel manufacturing plant, Rajpura, Punjab, India.The outcomes are very informative for the visited plant and provide suggestions for best management of inspection, maintenance, and operational strategies.

Keywords:

Reliability,Inspection,Variation in demand,Regenerative point technique,Semi-Markov process,Innovation.,

References:

I. Agnihotri, R. K., Ajit Khare, and Sanjay Jain. "Reliability analysis of a system of boiler used in readymade garment industry." Journal of Reliability and Statistical Studies (2008): 33-41, ISSN: 2229-5666
II. Balagurusamy, E. "Reliability Engineering. Tata McGraw-Hill Education." (1984), ISBN-10: 0070483396.
III. Behboudi, Zohreh, GR Mohtashami Borzadaran, and Majid Asadi. "Reliability modeling of two-unit cold standby systems: a periodic switching approach." Applied Mathematical Modelling 92 (2021), doi:176-195. 10.1016/j.apm.2020.11.001
IV. Balaso, FarandeBahubali, and Hanumant P. Jagtap. "Failure analysis steam turbine in sugar factory thermal power plant: a Review." IOP conference series: earth and environmental science. Vol. 1285. No. 1. IOP Publishing, (2024), doi: 10.1088/1755-1315/1285/1/012005
V. Cassady, C. Richard, and Edward A. Pohl. "Introduction to repairable systems modeling." Annual reliability and maintainability symposium. Vol. 49. (2003).
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