Journal Vol – 20 No – 12, December 2025

Abstract:

This study presents the design and evaluation of an ergonomic chair developed for Malaysian residential users through the integration of anthropometric data, ergonomic assessment, and experimental validation. Anthropometric dimensions from the Malaysian Anthropometric Database were applied to determine seat height, depth, width, and backrest dimensions suitable for local body proportions. Rapid Upper Limb Assessment (RULA) was conducted using digital manikins representing the 5th, 50th, and 95th percentiles to identify postures with minimal musculoskeletal risk. A 3D CAD model was created in SolidWorks, and finite element analysis (FEA) was performed to evaluate structural integrity under a 150 kg load. A full-scale prototype was validated using electromyography (EMG) testing involving 20 participants of varying height, weight, and body mass index (BMI). Root Mean Square (RMS) values of muscle activation were analyzed to assess comfort and fatigue. Results showed a RULA score of 2, strong structural stability, and low EMG activity, indicating minimal muscle strain. The integration of anthropometry, simulation, and EMG validation confirms the chair’s ergonomic suitability and establishes a framework for locally optimized furniture design.

Keywords:

Chair design,Comfort,Ergonomics,EMG,

References:

I. Aberdam, A., B. Pierrat, and F. Lavaste. “Evaluation of the Comfort of a Novel Chair Design Using Electromyography and Subjective Feedback.” Applied Ergonomics, vol. 82, 2020, pp. 102951, 10.1016/j.apergo.2019.102951.
II. Ansari, S., et al. “Design and Development of an Ergonomic Chair for Students in Educational Settings.” Health Scope, vol. 7, no. 4, 2018, pp. e60531. 10.5812/jhealthscope.60531.
III. Atef, B. “Ergonomics Study and Analysis of Workstations in Tunisian Mechanical Manufacturing.” Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 28, 2018, pp. 166-85. 10.1002/hfm.20713.
IV. Benden, M. R. “Ergonomic Design of the Home Environment.” Journal of Occupational and Environmental Medicine, vol. 60, no. 3, 2018, pp. 150-55. 10.1097/JOM.0000000000001227.
V. Bi, Z. M. “Computer Implementation.” Finite Element Analysis Applications: A Practical Guide to the FEM Project of Production Engineering Domains, edited by Z. M. Bi and Donald W. Mueller, Springer, 2018, pp. 227–80.
VI. Cifrek, M., et al. “Surface EMG Based Muscle Fatigue Evaluation in Biomechanics.” Clinical Biomechanics, vol. 24, no. 4, 2009, pp. 327–40. 10.1016/j.clinbiomech.2009.01.009.
VII. Delvaux, F., J. F. Kaux, and J. L. Croisier. “Lower Limb Muscle Injuries: Risk Factors and Preventive Strategies.” Science & Sports, vol. 32, no. 4, 2017, pp. 179–90. 10.1016/j.scispo.2017.02.003.
VIII. Dempsey, P. G., R. W. McGorry, and W. S. Maynard. “A Survey of Tools and Methods Used by Certified Professional Ergonomists.” Applied Ergonomics, vol. 36, 2005, pp. 489–503. 10.1016/j.apergo.2005.01.007.
IX. Ghazali, M. F., et al. “RULA and REBA Assessments in Computer Laboratories.” National Symposium on Advancements in Ergonomics and Safety (ERGOSYM2009), Universiti Malaysia Perlis, 2009, pp. 3-13.
X. Intertek. “The Definitive Guide to North American Furniture Testing.” Intertek Testing Services, NA, Inc., 2020. Intertek. https://www.intertek.com/furniture/.
XI. Khairuzzaman, M., and S. M. Aljunid. “An Ergonomic Evaluation of Office Chairs Using the Rapid Upper Limb Assessment (RULA) Tool.” International Journal of Industrial Ergonomics, vol. 79, 2020, pp. 103020. 10.1016/j.ergon.2020.103020.
XII. Kim, D. H., et al. “Effect of Rapid Upper Limb Assessment (RULA) Analysis on the Design of Dental Practitioner Chairs.” Journal of Physical Therapy Science, vol. 31, no. 4, 2019, pp. 292-95. 10.1589/jpts.31.292.
XIII. Kumar, S., and A. Khatavkar. “Ergonomic design of office chair: A review.” Journal of Ergonomics, vol. 9, no. 1, 2019, pp. 1-8.
XIV. Maruyama, T., N. Kajii, and M. Gotoh. “Electromyographic Evaluation of the Effect of Lumbar Support Shape and Armrest Height on Upper Body Muscle Activity and Perceived Comfort during Office Work.” Industrial Health, vol. 57, no. 3, 2019, pp. 300-10. 10.2486/indhealth.2018-0055.
XV. Mohamad, D., et al. “Development of a Malaysian Anthropometric Database.” World Engineering Congress 2010: Proceedings of the Conference on Manufacturing Technology and Management, Sarawak, Malaysia, 2010, pp. 4-5.
XVI. Murata, S. “Effect of Seat Shape with a Sloping Front Edge on Leg Blood Flow and Discomfort during Low-Intensity Tasks.” Journal of Occupational Health, vol. 61, no. 4, 2019, pp. 345-52. 10.1539/joh.18-0249-OA.
XVII. Pascual, S.A., and S. Naqvi. “An Investigation of Ergonomics Analysis Tools Used in Industry in the Identification of Work-Related Musculoskeletal Disorders.” International Journal of Occupational Safety and Ergonomics; JOSE, vol. 14, no. 2, 2008, pp. 237-45. 10.1080/10803548.2008.11076774.
XVIII. Shanmugam, M., et al. “Ergonomics design of office chair: A review.” IOP Conference Series: Materials Science and Engineering, vol. 1122, no. 1, 2021, pp. 012073.
XIX. Troiano, A., et al. “Assessment of Force and Fatigue in Isometric Contractions of the Upper Trapezius Muscle by Surface EMG Signal and Perceived Exertion Scale.” Gait & Posture, vol. 28, no. 2, 2008, pp. 179–86. 10.1016/j.gaitpost.2007.08.002.
XX. Wang, L. J., et al. “A Comparative Study of EMG Indices in Muscle Fatigue Evaluation Based on Grey Relational Analysis during All-Out Cycling Exercise.” BioMed Research International, 2018, pp. 1-8. 10.1155/2018/9341215.
XXI. Watanabe, M. “Optimal Reclining Angle of the Backrest of Office Chairs Based on Comfort and Muscle Activity.” Journal of Physical Therapy Science, vol. 32, no. 2, 2020, pp. 136-41. 10.1589/jpts.32.136.

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Abstract:

The primary factor influencing the wireless sensor network (WSN) is the energy consumption of the sensor node. One of the key factors influencing WSN energy consumption is the high power consumption and packet delivery ratio needed for WSN processing. The suggested Energy Efficient Spherical Grid Routing (EESGR) protocol reduces the node's energy consumption to meet the requirements. To choose the cluster heads, the WSN is clustered into a collection of nodes using the pillar k-means clustering method defined in the proposed protocol. One optimization algorithm inspired by nature, the ant lion, is used to create cluster heads for assessing energy consumption in WSNs. The behavior concept of the ant lion is utilized for choosing the best nodes for the selection of the cluster head. The multi-tier spherical grid routing proposed in the paper is used to grid the cluster head generated by the ant-lion optimization algorithm to evaluate the total energy consumed for processing the sensor network. The overall performance of this method is evaluated in Network Simulator 2 (NS2). The proposed method improves performance in throughput, end-to-end delay, packet delivery ratio, and energy consumption compared to the existing techniques.

Keywords:

Ant Lion Optimization Algorithm,Multi-Tier Spherical Grid Routing,Network Simulator,Pillar K-means Clustering,Wireless Sensor Networks (WSN),

References:

I. Azharuddin and Jana, “Particle swarm optimization for maximizing lifetime of wireless sensor networks”, Computers and Electrical Engineering, vol. 51, pp. 26-42, 2016.

II. Ch. Rambabu, V.V.K.D.V. Prasad and K. Satya Prasad, “A Visiting Center based Energy Efficient Data Collection Method for WSN”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, vol. 8, issue 3, pp.5152-5158, September 2019.
III. Darabkh and Zomot, “An improved cluster head selection algorithm for wireless sensor networks”, Proceedings of the 14th International Wireless Communications and Mobile Computing Conference (IWCMC) IEEE, pp. 65-70, 2018.
IV. El sayed, Sabbeh and Riad, “A distributed fault tolerance mechanism for self-maintenance of clusters in wireless sensor networks”, Arabian Journal for Science and Engineering, vol. 43, issue 12, pp.6891-6907, 2018.
V. G. Xie and F. Pan, “Cluster-based routing for the mobile sink in wireless sensor networks with obstacles”, IEEE Access, vol. 4, pp. 2019-2028, 2016.
VI. Huang, Hong, Zhao and Yuan, “An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks”, Cluster Computing, vol. 20, issue 4, pp.3071-3083, 2017.
VII. Jafarali Jassbi and Moridi, “Fault tolerance and energy efficient clustering algorithm in wireless sensor networks: FTEC”, Wireless Personal Communications, vol. 107, issue 1, pp.373-391, 2019.
VIII. Jiang, C.J., Shi, W.R. and Tang, “Energy-balanced unequal clustering protocol for wireless sensor networks”, The Journal of China Universities of Posts and Telecommunications, vol. 17, issue 4, pp.94-99, 2010.
IX. Kaur and Kumar, “Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks”, IEEE Sensors Journal, vol. 18, issue 11, pp.4614-4622, 2018.
X. Lohani and Varma, “Energy efficient data aggregation in mobile agent based wireless sensor network”, Wireless Personal Communications, vol. 89, issue 4, pp.1165-1176, 2016.
XI. R. Ahmad, R. Wazirali, and T. Abu-Ain, “Machine learning for wireless sensor networks security: An overview of challenges and issues,” Sensors, vol. 22, no. 13, p. 4730, Jun. 2022.
XII. S. Kumari and A. K. Tyagi, “Wireless sensor networks: An introduction,” in Digital Twin and Blockchain for Smart Cities. Beverly, MA, USA : Scrivener Publishing, 2024, pp. 495–528.
XIII. Wang, J., Cao, J., Ji, S. and Park, “Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks”, The Journal of Supercomputing, vol. 73, issue 7, pp.3277-3290,2017.
XIV. Wang, J., Cao, J., Sherratt, R.S. and Park, “An improved ant colony optimization-based approach with mobile sink for wireless sensor networks”, The Journal of Supercomputing, vol. 74, issue 12, pp.6633-6645. 2018.
XV. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1-10, 2000.

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Abstract:

This study explores a specialized type of domination in graphs known as fair domination. A fair dominating set (FDS) in a graph R is defined as a dominating set in which every non-member vertex is adjacent to an equal number of vertices within the set. The minimum size of such a set is referred to as the fair domination number, denoted γ_fd (R). We further examine how structural modifications, specifically edge subdivisions, affect this parameter. The fair domination subdivision number, denoted 〖Sd〗_(γ_fd)^+ (R) (or 〖Sd〗_(γ_fd)^- (R)), captures the smallest number of edge subdivisions required to increase or decrease the fair domination number, respectively. Our work focuses on computing these values for two graph families: cycles C_n (with n≥3) and Circulant graphs C_n (1,k),k=2,3. Through detailed analysis, we demonstrate how edge subdivisions impact the fairness condition in domination. To systematically explore fair domination in graphs, we adopt an algorithmic approach that facilitates efficient identification of fair dominating sets and computation of related parameters. Algorithmic techniques have been pivotal in graph theory, particularly in the study of domination-related problems. We introduce an efficient algorithm for identifying fair dominating sets and determining the fair domination number in Circulant graphs of the form〖 C〗_n(1,2) and C_n (1,3), offering insights into their underlying combinatorial structure.

Keywords:

Influence-based vertex covering,Uniform vertex influence,k-regular fair domination,Edge-splitting parameter,Subdivision for fair domination,

References:

I. Blažej, Václav, Jan Matyáš Křišťan, and Tomáš Valla: ‘Computing m-eternal domination number of cactus graphs in linear time’. arXiv. arXiv:2301.05155, 2023. https://doi.org/10.48550/arXiv.2301.05155.
II. Boehmer, Niclas, Tomohiro Koana, and Rolf Niedermeier: ‘A refined complexity analysis of fair districting over graphs.’ Autonomous Agents and Multi-Agent Systems. Vol. 37(1), pp: 13, 2023. 10.48550/arXiv.2102.11864.
III. Caro, Yair, Adriana Hansberg, and Michael Henning: ‘Fair domination in graphs’. Discrete Mathematics. Vol. 312(19), pp: 2905-2914, 2012. 10.1016/j.disc.2012.05.006.
IV. Casado, Alejandra, Jesús Sánchez-Oro, and Anna Martínez-Gavara: ‘Heuristics for the weighted total domination problem’. TOP: An Official Journal of the Spanish Society of Statistics and Operations Research. Vol. 33(2), pp: 395–436, 2025 10.1007/s11750-025-00695-1.
V. Dejter, Italo J: ‘Perfect domination in regular grid graphs’. Australasian Journal of Combinatoricsz. Vol. 42, pp: 99–114, 2007. 10.48550/arXiv.0711.4343.
VI. Enriquez, Enrico, et al : ‘Domination in fuzzy directed graphs’. Mathematics. Vol. 9(17), pp: 2143, 2021. 10.3390/math9172143.
VII. Hajian, Majid, and N. Jafari Rad: ‘Trees and unicyclic graphs with large fair domination number’. Util. Math. Vol. 112, 2022.
VIII. Hajian, Majid, and Nader Jafari Rad: ‘Fair domination number in cactus graphs’. Discussiones Mathematicae Graph Theory. Vol. 39(2), pp: 489-503, 2019.
IX. Hansberg, Adriana: ‘Reviewing some results on fair domination in graphs’. Electronic Notes in Discrete Mathematics. Vol. 43, pp: 367-373, 2013. https://doi.org/10.1016/j.endm.2013.07.054.
X. Harary, Frank. Graph theory (on Demand Printing of 02787). CRC Press, 2018. 10.1201/9780429493768.
XI. Hatami, Hamed, and Pooya Hatami: ‘Perfect dominating sets in the Cartesian products of prime cycles’. The Electronic Journal of Combinatorics, Vol. 14(1), pp: N8, 2007. https://doi.org/10.37236/1009.
XII. Haynes, Teresa W., Stephen Hedetniemi, and Peter Slater: ‘Fundamentals of domination in graphs’. CRC press, 2013. 10.1201/9781482246582.
XIII. Henning, Michael A., Arti Pandey, and Vikash Tripathi: ‘Complexity and algorithms for semipaired domination in graphs’. Theory of Computing Systems, Vol. 64(7), pp: 1225-1241, 2020. 10.48550/arXiv.1904.00964.
XIV. Inza, Ernesto Parra, et al: ‘Algorithms for the global domination problem’. Computers & Operations Research. Vol. 173, pp: 106876, 2025. 10.1016/j.cor.2024.106876.
XV. Jafari Rad, Nader, et al: ‘Total domination in cubic Knödel graphs’. Communications in Combinatorics and Optimization. Vol. 6(2), pp: 221-230, 2021. 10.22049/cco.2020.26793.1143.
XVI. Joseph, J. Paulraj, and S. Arumugam: ‘Domination in subdivision graphs’. J. Indian Math. Soc. Vol. 62, pp: 274-282, 1996.
XVII. Kumar, J. Pavan, and P. Venkata Subba Reddy: ‘Algorithmic aspects of some variants of domination in graphs’. Analele Stiint. Ale Univ. Ovidius Constanta Ser. Mat. Vol. 28(3), pp: 153-170, 2020. 10.48550/arXiv.2002.00002.
XVIII. Kumar, J. Pavan, P. Venkata Subba Reddy, and S. Arumugam: ‘Algorithmic complexity of secure connected domination in graphs’. AKCE International Journal of Graphs and Combinatorics. Vol. 17(3), pp: 1010-1013, 2020. 10.48550/arXiv.2002.00002.
XIX. Lin, Ching-Chi, Cheng-Yu Hsieh, and Ta-Yu Mu: ‘A linear-time algorithm for weighted paired-domination on block graphs’. Journal of Combinatorial Optimization. Vol. 44(1), pp: 269-286, 2022. 10.1007/s10878-021-00767-5.
XX. Miranda, Aldwin T., and Rolito G. Eballe : ‘Domination defect for the join and corona of graphs’. Applied Mathematical Sciences. Vol. 15(12), pp: 615-623, 2021. 10.12988/ams.2021.914597.
XXI. Mu, Ta-Yu, and Ching-Chi Lin: ‘Optimal Algorithm for Paired-Domination in Distance-Hereditary Graphs’. arXiv. arXiv:2411.19476, 2024. 10.48550/arXiv.2411.19476.

XXII. Navamani. G and Reena Mercy M A: ‘Fair domination number of some graphs’. Preprint.
XXIII. Novak, Tina, and Janez Žerovnik: ‘A Linear Time Algorithm for Weighted k-Fair Domination Problem in Cactus Graphs’. Operations Research Forum Cham: Springer International Publishing. Vol. 3(3), 2022. 10.1007/s43069-022-00154-8.
XXIV. Pushpam, P. Roushini Leely, and G. Navamani: ‘Eternal m-security in certain classes of graphs’. J. Combin. Math. Combin. Vol. 92, pp: 25-38, 2015.
XXV. Rad, Nader Jafari: ‘Domination in circulant graphs’. Analele Stiintifice Ale Universitatii Ovidius Constanta, Seria Matematica. Vol. 17(1), pp: 169-176, 2019.
XXVI. Swaminathan, V., et al: ‘Outer complete fair domination in graphs’. Discrete Mathematics, Algorithms and Applications. Vol. 14(03), pp: 2150126, 2022. 10.1142/S1793830921501263.

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Abstract:

The Internet of Things (IoT) links billions of devices, boosts innovation, shares information effortlessly, and is reshaping various industries. The most common Distributed Denial of Service (DDoS) attacks target all layers in the IoT architecture. Even though easy to execute, these sorts of attacks may severely harm targeted systems and networks. This Novel hybrid model uses Bagged Long Short-Term Memory (LSTM) and Gradient Boosting (GB) to address large dimensionality, various feature dimensions, low classification accuracy, and high false positive rates in raw traffic data to improve IoT security against DDoS attacks. To reduce input information redundancy, the Boruta-Pearson Feature Selector (BPFS) gathers key features as model inputs. The Bagged-LSTM design minimises variance to detect anomalies, while Gradient Boosting improves prediction accuracy. The CIC-ISD2017 and CIC DDoS2019 datasets were used to test the hybrid model. Experimental results show that the recommended model outperforms current models with an accuracy of 99%. It is impossible to completely protect your server from these threats, but by using the techniques discussed here, these attacks can be prevented, and the server can focus on fulfilling legitimate requests rather than unauthentic ones.

Keywords:

DDoS attacks,Gradient Boosting (GB),IoT security,long short-term memory (LSTM),

References:

I. Ade, J. V. “Ensemble Learning Methods for DDoS Attack Detection in Cloud Environments: A Comprehensive Review.” International Journal of Science and Engineering Applications, vol. 13, no. 5, 2024, pp. 40–45.
II. Ali, T. E., Chong, Y. W., and S. Manickam. “Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review.” Applied Sciences, vol. 13, no. 5, 2023, p. 3183.
III. Al-kahtani, M. S., Z. Mehmood, T. Sadad, I. Zada, G. Ali, and M. ElAffendi. “Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model.” Intelligent Automation & Soft Computing, vol. 37, no. 2, 2023.
IV. Alkahtani, H., and T. H. Aldhyani. “Botnet Attack Detection by Using CNN‐LSTM Model for Internet of Things Applications.” Security and Communication Networks, vol. 2021, no. 1, 2021, p. 3806459.
V. A. A. “Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing.” Journal of Cyber Security and Mobility, 2022, pp. 265–278.
VI. Bårli, E. M., A. Yazidi, E. H. Viedma, and H. Haugerud. “DoS and DDoS Mitigation Using Variational Autoencoders.” Computer Networks, vol. 199, 2021, p. 108399.
VII. Cheng, J. R., et al. “DDoS Attack Detection via Multi-Scale Convolutional Neural Network.” Computers, Materials & Continua, vol. 62, no. 3, 2020, pp. 1317–1333.
VIII. DDoS Evaluation Dataset (CIC-DDoS2019). University of New Brunswick, Saint John, NB, Canada, 2019.
IX. Goud, K. S., and G. S. Rao. “Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion.” Proceedings of the Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), IEEE, Jan. 2024, pp. 1–10.
X. Issa, A. S. A., and Z. Albayrak. “DDoS Attack Intrusion Detection System Based on Hybridization of CNN and LSTM.” Acta Polytechnica Hungarica, vol. 20, no. 2, 2023, pp. 1–19.
XI. Koay, A., A. Chen, I. Welch, and W. K. G. Seah. “A New Multi-Classifier System Using Entropy-Based Features in DDoS Attack Detection.” Proceedings of the International Conference on Information Networking (ICOIN), Chiang Mai, 2018, pp. 162–167.
XII. Maheshwari, A., B. Mehraj, M. S. Khan, and M. S. Idrisi. “An Optimized Weighted Voting-Based Ensemble Model for DDoS Attack Detection and Mitigation in SDN Environment.” Microprocessors and Microsystems, vol. 89, 2022, p. 104412.
XIII. Mall, R., K. Abhishek, S. Manimurugan, A. Shankar, and A. Kumar. “Stacking Ensemble Approach for DDoS Attack Detection in Software-Defined Cyber–Physical Systems.” Computers and Electrical Engineering, vol. 107, 2023, p. 108635.
XIV. Mittal, M., K. Kumar, and S. Behal. “Deep Learning Approaches for Detecting DDoS Attacks: A Systematic Review.” Soft Computing, vol. 27, no. 18, 2023, pp. 13039–13075.
XV. Muthukumar, S., and A. K. Ashfauk Ahamed. “A Novel Framework of DDoS Attack Detection in Network Using Hybrid Heuristic Deep Learning Approaches with Attention Mechanism.” Journal of High-Speed Networks, Preprint, 2024, pp. 1–27.
XVI. Okey, O. D., S. S. Maidin, P. Adasme, R. Lopes Rosa, M. Saadi, D. Carrillo Melgarejo, and D. Zegarra Rodríguez. “BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning.” Sensors, vol. 22, no. 19, 2022, p. 7409.

XVII. Priyadarshini, I., P. Mohanty, A. Alkhayyat, R. Sharma, and S. Kumar. “SDN and Application Layer DDoS Attacks Detection in IoT Devices by Attention‐Based Bi‐LSTM‐CNN.” Transactions on Emerging Telecommunications Technologies, vol. 34, no. 11, 2023.
XVIII. Singh, C., and A. K. Jain. “A Comprehensive Survey on DDoS Attacks Detection & Mitigation in SDN-IoT Network.” e-Prime – Advances in Electrical Engineering, Electronics and Energy, 2024, p. 100543.
XIX. Subrmanian, M., K. Shanmugavadivel, P. S. Nandhini, and R. Sowmya. “Evaluating the Performance of LSTM and GRU in Detection of Distributed Denial of Service Attacks Using CICDDoS2019 Dataset.” Proceedings of the 7th International Conference on Harmony Search, Soft Computing and Applications: ICHSA 2022, Springer Nature Singapore, Sept. 2022, pp. 395–406.
XX. Tiwari, R. S. “Model Evaluation.” Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications, 2022, pp. 33–100.
XXI. Umar, M. A., Z. Chen, K. Shuaib, and Y. Liu. “Effects of Feature Selection and Normalization on Network Intrusion Detection.” Authorea Preprints, 2024.
XXII. Wang, W., Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, and M. Zhu. “HAST-IDS: Learning Hierarchical Spatial–Temporal Features Using Deep Neural Networks to Improve Intrusion Detection.” IEEE Access, vol. 6, 2018, pp. 1792–1806.
XXIII. Ye, J., X. Cheng, and J. Zhu. “A DDoS Attack Detection Method Based on SVM in Software-Defined Network.” Security and Communication Networks, vol. 4, July 2018, pp. 1–8.
XXIV. Yousuf, O., and R. N. Mir. “DDoS Attack Detection in Internet of Things Using Recurrent Neural Network.” Computers and Electrical Engineering, vol. 101, 2022, p. 108034.
XXV. Yu, P., and C. Li. “DDoS Attack Detection Method Based on Random Forest.” Applied Research in Computers, vol. 34, no. 10, 2017, pp. 3068–3072.
XXVI. Yulianto, A., P. Sukarno, and N. A. Suwastika. “Improving AdaBoost-Based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset.” Journal of Physics: Conference Series, vol. 1192, 2019, art. no. 012018.
XXVII. Zhang, Y., Y. Liu, X. Guo, Z. Liu, X. Zhang, and K. Liang. “A BiLSTM-Based DDoS Attack Detection Method for Edge Computing.” Energies, vol. 15, no. 21, 2022, p. 7882.

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Abstract:

In this paper, an availability analysis of a gas compressor extensively used in the oil and gas industry is presented. It aims to investigate possible causes of compressor unavailability and to obtain various reliability indices that reflect the overall system's operational capabilities. Results demonstrate the impact of operating conditions and various faults on compressor reliability, with sensitivity analysis revealing how variations in failure and repair rates affect the overall system’s reliability. The analysis utilizes real data from an oil and gas exploration and production company. The findings offer insights for enhancing compressor robustness and suggest future research directions to address the system’s reliability challenges, contributing to more resilient oil and gas infrastructure.

Keywords:

availability,Markov processes,sensitivity analysis,regenerative processes,reliability analysis.,

References:

I. A. G. Mathew, S. M. Rizwan, M. C. Majumder and K. P. Ramachandran, “Reliability modelling and analysis of a two-unit continuous casting plant,” Journal of the Franklin Institute, vol. 348, no. 7, pp. 1488-1505, 2011.
II. G. Taneja, V. Khurana and S. M. Rizwan, “Economic analysis of a reliability model for two programmable logic controller cold standby system with four types of failure,” Pure Applied Mathematika Sciences, vol. 63, no. 1-2, pp. 65-78, 2006.
III. K. B. Misra, Handbook of Performability Engineering, 1st ed. Springer: London, 2008.
IV. K. Sachdeva, G. Taneja and A. Manocha, “Sensitivity and economic analysis of an insured system with extended conditional warranty,” Reliability: Theory & Applications, vol. 17, no. 3(69), pp. 315-327, 2022.
V. N. Padmavathi, S. M. Rizwan, A. Pal and G. Taneja, “Probabilistic analysis of a desalination plant with major and minor failures and shutdown during winter season,” International Journal of Scientific and Statistical Computing, vol. 5, no. 1, pp. 15-23, 2014.
VI. Nabila Al Balushi, S. M. Rizwan, S. Z. Taj and Waleed Al Khairi, “Probabilistic analysis of power transformers in a power distribution company with six types of failures and inspection,” International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 15-22, 2024.
VII. S. M. Rizwan, J. V. Thanikal, N. Padmavathi and H. Yazidi, “Reliability and availability analysis of an anaerobic batch reactor treating fruit and vegetable waste,” International Journal of Applied Engineering Research, vol. 10, no. 24, pp. 44075-44079, 2015.
VIII. S. Z. Taj, “Performance and cost benefit analysis of reliability models for a cable plant,” Ph.D. dissertation, Glasgow Caledonian University, Glasgow, Scotland, U. K., 2023.
IX. S. Z. Taj, S. M. Rizwan and K. Sachdeva, “Reliability and sensitivity analysis of a wastewater treatment plant operating with two blowers as a single system,” in Reliability Engineering for Industrial Processes: An Analytics Perspective. Cham: Springer Nature Switzerland, 2024, pp. 19-39.
X. Yaqoob Al Rahbi, S. M. Rizwan, B. M. Alkali, A. Cowell and G. Taneja, “Reliability analysis of a rodding anode plant in aluminum industry with multiple units failure and a single repairman,” International Journal of System Assurance Engineering and Management, vol. 10, pp. 97-109, 2019.

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Abstract:

This study investigates the enhancement of flexural strength of high-performance concrete (HPC) beams using basalt fiber reinforced polymer (BFRP) embedded internally at different depths. Four types of beam samples were tested: BFRP placed directly on the bottom (CB0), and BFRP placed at 0.75 cm (CB0.75), 1.25 cm (CB1.25), and 2.25 cm (CB2.25) from the bottom. The concrete mixture, which resembled ultra-high-performance concrete, included binder, fine and coarse aggregates, glass powder, microsilica, and a plasticizer. The results showed that BFRP significantly improved the flexural strength and ductility compared to the control samples without BFRP reinforcement. Optimum performance was achieved by placing the BFRP at 1.25 cm from the bottom (CB1.25), which demonstrated an increase in flexural strength by 1088% (653 kN/m2) and displacement by 0.225 mm compared to the control samples, indicating a balanced distribution of strength and stress. Large distances (e.g., CB2.25) reduce the effectiveness, highlighting the importance of BFRP proximity to tension zones.

Keywords:

Basalt Fiber Reinforced Polymer,Flexural Strength,Ductility,High-Performance Concrete,Reinforced Concrete Beam,

References:

I. Adak D, Sarkar M, Mandal S (2017) Structural performance of nano-silica modified fly-ash based geopolymer concrete. Construction and Building Materials, 4: 430–439. 10.1016/j.conbuildmat.2016.12.111
II. Alaa Hasan H, Neaz Sheikh M, Hadi MNS (2019) Maximum axial load carrying capacity of Fibre Reinforced-Polymer (FRP) bar reinforced concrete columns under axial compression. Structures, 19: 227–233. 10.1016/j.istruc.2018.12.012
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Abstract:

The present study explores the feasibility of utilizing Bergamot Peel Oil (BPO) as a renewable alternative to conventional diesel fuel in a compression ignition engine. Before engine testing, the physicochemical properties of BPO were tested. FTIR analysis confirmed the presence of oxygenated functional groups such as esters and carbonyl compounds, while CHNS analysis revealed a significant oxygen content, supporting improved combustion characteristics. GC-MS analysis identified major fatty acid methyl esters contributing to the high volatility and calorific value of BPO. Experimental investigations were conducted at varying blending ratios (BPO10, BPO20, BPO30, BPO40, and BPO50) without any engine modifications, evaluating performance, combustion, and emission parameters against baseline diesel. Results indicated an improvement in brake thermal efficiency (BTE) by 3–6% for BPO30–BPO40 blends, while brake specific fuel consumption (BSFC) reduced by up to 9% for higher blends, attributed to better energy content and oxygenation. In-cylinder analysis revealed increased peak pressure and rate of pressure rise (RoPR) with BPO addition, with BPO50 recording the highest peak due to superior oxidation and localized high temperatures. Ignition delay showed a slight increase for higher blends due to lower cetane number, though overall combustion duration remained comparable to diesel. On the emissions front, smoke opacity was significantly reduced by 14–17% for BPO50 owing to enhanced soot oxidation. Carbon monoxide (CO) and unburned hydrocarbons (HC) decreased by 8–12% across all blends, while NOx emissions exhibited a 6–10% rise for higher blends due to an increase in in-cylinder temperatures and oxygen availability. The findings suggest that BPO, with its oxygenated nature and favorable volatility, can partially replace diesel fuel without major engine modifications, particularly in blends up to BPO40, ensuring improved efficiency and cleaner combustion.

Keywords:

Bergamot peel oil,Fuel characterization,Diesel,Engine,Performance,

References:

I. Ashok, B., R. Thundil Karuppa Raj, et al. “Lemon Peel Oil – A Novel Renewable Alternative Energy Source for Diesel Engine.” Energy Conversion and Management 139 (2017): 110–121. 10.1016/j.enconman.2017.02.049
II. Chen, Hui et al. “The Effect of a Pine Oil/Diesel Blend on the Particulate Emission Characteristics of a Diesel Engine under a Pre-Injection Strategy with EGR.” Sustainable Energy & Fuels 7.15 (2023): 3644–3653. Web. 17 Aug. 2025. https://pubs.rsc.org/en/content/articlehtml/2023/se/d3se00581j
III. Chivu, Robert Mădălin et al. “Assessment of Engine Performance and Emissions with Eucalyptus Oil and Diesel Blends.” Energies 2024, Vol. 17, Page 3528 17.14 (2024): 3528. Web. 17 Aug. 2025. https://www.mdpi.com/1996-1073/17/14/3528/htm
IV. Doppalapudi, Arun Teja, Abul Kalam Azad, and Mohammad Masud Kamal Khan. “Exergy, Energy, Performance, and Combustion Analysis for Biodiesel NOx Reduction Using New Blends with Alcohol, Nanoparticle, and Essential Oil.” Journal of Cleaner Production 467 (2024): 142968. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/pii/S095965262402417X
V. Duraisamy, Ganesh, Murugan Rangasamy, and Abul K. Hossain. “A Study on Flexible Dual-Fuel and Flexi Combustion Mode Engine to Mitigate NO, Soot and Unburned Emissions.” Fuel 322 (2022): 124276. Web. 19 Aug. 2025. https://www.sciencedirect.com/science/article/abs/pii/S0016236122011280
VI. Ellappan, Sivakumar, and Silambarasan Rajendran. “A Comparative Review of Performance and Emission Characteristics of Diesel Engine Using Eucalyptus-Biodiesel Blend.” Fuel 284 (2021): 118925. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/abs/pii/S0016236120319219
VII. Gad, M. S. et al. “Combustion Characteristics of a Diesel Engine Running with Mandarin Essential Oil -Diesel Mixtures and Propanol Additive under Different Exhaust Gas Recirculation: Experimental Investigation and Numerical Simulation.” Case Studies in Thermal Engineering 26 (2021): 101100. Web. 17 Aug. 2025.
https://www.sciencedirect.com/science/article/pii/S2214157X2100263X
VIII. Karthickeyan, V. et al. “Simultaneous Reduction of NOx and Smoke Emissions with Low Viscous Biofuel in Low Heat Rejection Engine Using Selective Catalytic Reduction Technique.” Fuel 255 (2019): 115854. Print.
IX. Nanthagopal, K. et al. “Lemon Essential Oil – A Partial Substitute for Petroleum Diesel Fuel in Compression Ignition Engine.” International Journal of Renewable Energy Research 7.2 (2017): 467–475. Print.
X. Nguyen, Van Nhanh et al. “Engine Behavior Analysis on a Conventional Diesel Engine Combustion Mode Powered by Low Viscous Cedarwood Oil/Waste Cooking Oil Biodiesel/Diesel Fuel Mixture – An Experimental Study.” Process Safety and Environmental Protection 184 (2024): 560–578. Web. 17 Aug. 2025.
https://www.sciencedirect.com/science/article/abs/pii/S0957582024001253
XI. Patel, Ashok K., Basant Agrawal, and B. R. Rawal. “Assessment of Diesel Engine Performance and Emission Using Biodiesel Obtained from Eucalyptus Leaves.” European Journal of Sustainable Development Research 7.1 (2023): em0210. Web. 17 Aug. 2025.
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XIV. Y. Alex, et al. “Study of Engine Performance and Emission Characteristics of Diesel Engine Using Cerium Oxide Nanoparticles Blended Orange Peel Oil Methyl Ester.” Energy Nexus 8 (2022): 100150. Web. 17 Aug. 2025. https://www.sciencedirect.com/science/article/pii/S277242712200105X

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Abstract:

The Runge-Kutta method, and especially its fourth-order variant (RK4), is perhaps the most widely adopted method for solving ordinary differential equations (ODEs) and their systems. This paper deals specifically with the RK4 method to explain a system of first-order differential equations, and the ability of the method to converge and stabilize positive solutions. It is well known that standard RK4 is both accurate and stable, but to particularly maintain positivity of solutions, where the model represents physical quantities that must be non-negative, such as populations or concentrations, often requires extra techniques. This paper discusses theoretically the RK4 method and systems, their execution, the need for retention of positivity, and methodologies for retention of positivity. Several illustrative examples are included to demonstrate the application of the method and the difficulty of maintaining positivity as well.

Keywords:

Runge-Kutta method,fourth-order,systems of first-order differential equations,positive solutions,

References:

I. Amirul. Md. Islam et al. “Accurate Solutions of Initial Value Problems for Ordinary Differential Equations with the Fourth Order Runge Kutta Method.” Journal of Mathematics Research, 7 (2015): 41. 10.5539/jmr.v7n3p41.
II. Bazuaye, F. E. “A new 4th order hybrid Runge-Kutta methods for solving initial value problems (IVPs).” Pure and Applied Mathematics Journal 7.6 (2018): 78-87.
https://www.sciencepublishinggroup.com/article/10.11648/j.pamj.20180706.11
III. Blanes. S. et al. “Positivity-preserving methods for ordinary differential equations.” ESAIM: Mathematical Modelling and Numerical Analysis (2021). 10.1051/m2an/2022042.
IV. Botelho. F. et al. “On the Numerical Solution of First Order Ordinary Differential Equation Systems.” (2020): 498-511. 10.1201/9780429343315-29
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VIII. Ibrahim, Salisu. “Solution of First-Order Differential Equation Using Fourth-Order Runge-Kutta Approach and Adams Bashforth Methods.” International Journal on Recent and Innovation Trends in Computing and Communication 11.11 (2023).https://ijritcc.org/index.php/ijritcc/article/view…
IX. Jemal Demsie Abraha et al. “Comparison of Numerical Methods for System of First Order Ordinary Differential Equations.” Pure and Applied Mathematics Journal, 9 (2020): 32. 10.11648/j.pamj.20200902.11.
X. Jingwei Hu et al. “A Second-Order Asymptotic-Preserving and Positivity-Preserving Exponential Runge-Kutta Method for a Class of Stiff Kinetic Equations.” Multiscale Model. Simul., 17 (2018): 1123-1146. 10.1137/18m1226774.
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XII. Shior. M., et al. “Solution of First Order Ordinary Differential Equations Using Fourth Order Runge-Kutta Method with MATLAB.” International Journal of Mathematics and Statistics Studies 12.1 (2024): 54-63. 10.37745/ijmss.13
XIII. Stephan Nüßlein et al. “Positivity-Preserving Adaptive Runge-Kutta Methods.” ArXiv, abs/2005.06268 (2020):155-179. 10.2140/camcos.2021.16.155.
XIV. Toparkus. H. et al. “First-order systems of linear partial differential equations: normal forms, canonical systems, transform methods.” Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica, 13 (2014): 109 – 132. 10.2478/aupcsm-2014-0009.
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XVI. Zaileha Md Ali et al. “Lotka-Volterra Model of Wastewater Treatment in Bioreactor System using 4th Order Runge-Kutta Method.” Science Letters (2022). 10.24191/sl.v16i1.15284.

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Abstract:

Maximum Power Point Tracking (MPPT) techniques is efficient technique implemented high photovoltaic power generation in modern power system. This paper will present a MPPT strategy with a Seagull Optimization Algorithm (SOA)-based strategy and high-gain Voltage-Multiplier Coupled Quadric Boost Converter to implement a high-efficiency power extraction in PV systems. The SOA takes advantage of the hunting nature of seagulls so that the operating point of the PV array can be optimised and that the global maximum power point can be reached within seconds even in dynamic irradiance and temperature conditions. Combining this smart MPPT approach with a high-gain quadratic boost converter can achieve large voltage step-up on low PV input to decrease converter stress and increase energy harvesting. Through simulation, the proposed method proves to have a higher tracking speed, efficiency, and stability relative to existing ones (Perturb and Observe) (P&O) and Incremental Conductance (IncCond). The SOA-based MPPT is able to effectively prevent local maxima under partial shading conditions to generate optimal power extraction. The offered system demonstrates the high increase in the general energy efficiency, and it can be applied to both grid-connected and stand-alone PV applications. This combination of smart optimization and sophisticated converter design offers a potential remedy on the extraction of the best performance of a PV system under real operating conditions.

Keywords:

Photovoltaic Systems,MPPT,Seagull Optimization Technique,High-Efficiency Power Extraction,High-Gain Quadratic Boost Converter ,

References:

I. Al-Samawi, A. A., Atiyah, A. S., & Al-Jrew, A. H. (2025). Power Optimization of Partially Shaded PV System Using Interleaved Boost Converter-Based Fuzzy Logic Method. Eng, 6(8), 201. 10.3390/eng6080201
II. A.S. Valarmathy, M. Prabhakar. (2024). High gain interleaved boost-derived DC-DC converters – A review on structural variations, gain extension mechanisms and applications. e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 8, 2024, 100618, ISSN 2772-6711. 10.1016/j.prime.2024.100618
III. B. P. Ganthia, R. Pradhan, S. Das and S. Ganthia, “Analytical study of MPPT based PV system using fuzzy logic controller,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 2017, pp. 3266-3269. 10.1109/ICECDS.2017.8390063.
IV. B. P. Ganthia, S. Mohanty, P. K. Rana and P. K. Sahu, “Compensation of voltage sag using DVR with PI controller,” 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 2138-2142. 10.1109/ICEEOT.2016.7755068.
V. Chakole, N., Remamany, K. P., Mohan, G., Sasirekha, P., Kumar, N. M. G., Kumar, C. R., … & Ganthia, B. P. (2025). Optimal Energy Management for Hybrid PV-Wind-Battery Microgrids through Markov Decision Processes Technique. International Journal of Smart Grid-ijSmartGrid, 9(3), 127-145.
VI. Chalh, A., chaibi, R., Hammoumi, A.E. et al. (2022). A novel MPPT design based on the seagull optimization algοrithm for phοtovοltaic systems operating under partial shading. Sci Rep 12, 21804. 10.1038/s41598-022-26284-x
VII. Chikezie M. Emeghara, Satish M. Mahajan, Ali Arzani, Two-stage photovoltaic system with a high-gain fifth-order boost converter, e-Prime – Advances in Electrical Engineering, Electronics and Energy, Volume 13, 2025, 101038, ISSN 2772-6711. 10.1016/j.prime.2025.101038
VIII. Ganthia, B. P., Panda, S., Remamany, K. P., Chaturvedi, A., Begum, A. Y., Mohan, G., … & Ishwarya, S. (2025). Experimental techniques for enhancing PV panel efficiency through temperature reduction using water cooling and colour filters. Electrical Engineering, 1-27.
IX. G.Veera Sankara Reddy, S. Vijayaraj, Optimizing high voltage gain interleaved boost converters for PV and wind systems using hybrid deep learning with bitterling fish and secretary bird algorithms, Franklin Open, Volume 11, 2025, 100291, ISSN 2773-1863, 10.1016/j.fraope.2025.100291
X. Himani Daulat, Krishna Chauhan & Tarun Varma. (2025). Empowering quadrature mirror filter bank architectures: dynamic-grey wolf optimization approach for elevated filter orders. Engineering Optimization 57:6, pages 1575-1603.
XI. K Rajaram & R Kannan. (2024). Design and implementation of adaptive pufferfish optimization algorithm based efficient MPPT and DC-DC boost converter for agriculture applications under partial shading conditions. Intelligent Decision Technologies 19:2, pages 1074-1090.
XII. Keerthi Sonam Soma, Balamurugan Ramadoss & Karuppiah Natarajan. (2024). Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems. Recent Advances in Electrical & Electronic Engineering 17:10, pages 1023-1041.
XIII. Marlin S & Sundarsingh Jebaseelan. (2024). A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems. Sustainable Computing: Informatics and Systems 41, pages 100946.
XIV. M. Mohanty, N. Nayak, B. P. Ganthia and M. K. Behera, “Power Smoothening of Photovoltaic System using Dynamic PSO with ESC under Partial Shading Condition,” 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), Bhubaneswar, India, 2023, pp. 675-680. 10.1109/APSIT58554.2023.10201763.
XV. Ming-Wei Li, Rui-Zhe Xu, Zhong-Yi Yang, Wei-Chiang Hong, Xiao-Gang An & Yi-Hsuan Yeh. (2024). Optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm. Applied Soft Computing 152, pages 111197.
XVI. P. K. Sahu, A. Mohanty, B. P. Ganthia and A. K. Panda, “A multiphase interleaved boost converter for grid-connected PV system,” 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), Durgapur, 2016, pp. 1-6. 10.1109/MicroCom.2016.7522539.
XVII. S. J. Rubavathy et al., “Smart Grid Based Multiagent System in Transmission Sector,” 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2021, pp. 1-5. 10.1109/ICIRCA51532.2021.9544644.
XVIII. Shih-Cheng Horng & Shieh-Shing Lin. (2022). Incorporate seagull optimization into ordinal optimization for solving the constrained binary simulation optimization problems. The Journal of Supercomputing 79:5, pages 5730-5758.
XIX. Soham Chakraborty, Amritesh Kumar, A Multilevel Based High Gain Switched Inductor Quadratic DC-DC Boost Converter, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 448-453, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.074
XX. Sourya Kumar Nej, S. Sreejith, Indrojeet Chakraborty, Dual-Output Multistage Switched-Capacitor Quadratic Boost (MSC-QBC) DC-DC Converter for Solar Photovoltaic Application, IFAC-PapersOnLine, Volume 55, Issue 1, 2022, Pages 965-970, ISSN 2405-8963. 10.1016/j.ifacol.2022.04.159
XXI. Vimal Kumar Pathak, Swati Gangwar & Mithilesh K. Dikshit. (2025). A Comprehensive Survey on Seagull Optimization Algorithm and Its Variants. Archives of Computational Methods in Engineering 32:6, pages 3651-3685.
XXII. Yancang Li, Weizhi Li, Qiuyu Yuan, Huawang Shi & Muxuan Han. (2023). Multi-strategy Improved Seagull Optimization Algorithm. International Journal of Computational Intelligence Systems 16:1.

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