Archive

Abstract:

This study explores the application of logistic regression in analyzing binary outcomes within a randomized block design framework. Specifically, it focuses on a binary variable representing seminar evaluations, which can cause two outcomes: “useful” (success) or “not useful” (failure). Logistic regression, as developed by Cox (1972), is utilized to model the probability of a successful outcome based on various predictor variables associated with different treatment groups. This study's main goal is to evaluate the variables that affect seminar success in a variety of research scholar groups. Data for this study were collected during the 2023-24 academic year, where expert evaluations were gathered to understand their perceptions of the seminar's value. The logistic regression model's relevance is assessed using the likelihood ratio test as the decision rule in the study. The findings show significant differences in the evaluations of research scholars, revealing key insights into the factors that affect perceived seminar effectiveness. These results underscore the utility of logistic regression as a valuable analytical tool in educational assessments and provide implications for enhancing future seminar designs.

Keywords:

Logistic regression,binary outcomes,seminar evaluation,likelihood ratio test.,

References:

I. Cox, D. R. (1972). Regression models and life tables. Journal of the
Royal Statistical Society: Series B (Methodological), 34(2), 187-220.
10.1111/j.2517-6161.1972.tb00899.x
II. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage
Publications. ISBN: 978-1-4462-4918-5.
III. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression.
John Wiley & Sons. 10.1002/0471722146

IV. Johnson, M., & Liu, R. (2022). The role of logistic regression in
behavioral science research. Behavioral Research Methods, 54(1), 22-35.
10.3758/s13428-021-01742-4
V. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN:
978-0-7619-1904-9.
VI. Smith, J., Anderson, K., & Lee, T. (2021). Evaluating educational programs using logistic regression. Educational Research Review, 16(3),
112-128. 10.1016/j.edurev.2021.100383

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Abstract:

Objective: Automated seizure detection from scalp electroencephalography(EEG) remains challenging because EEG signals are non-stationary, noisy, highly imbalanced, and vary substantially across patients. This study aimed to develop a robust deep learning framework for seizure detection under clinically relevant, leakage-controlled evaluation settings. Methods: We proposed a frequency-aware CNN–Vision Transformer (FA-CNNViT) framework integrating deterministic dataset harmonization, subject-wise leakage-controlled cross-validation, split-specific preprocessing, and post-split window generation. The model combines convolutional encoding for local morphological features with transformer-based modeling of long-range dependencies. An adaptive multi-stream feature fusion module was used to preserve temporal, spectral, and spatial information. Asymmetric focal loss addressed class imbalance, and Monte Carlo dropout was used to estimate predictive uncertainty. Results: On the CHB-MIT dataset, FA-CNNViT achieved 99.13% accuracy, 99.10% sensitivity, 99.47% specificity, 99.55% F1-score, and 99.74% ROC-AUC. In the cross-subject setting on the Turkish EEG dataset, it achieved 89.98% accuracy, 87.41% sensitivity, 88.61% specificity, 87.44% F1- score, and 88.79% ROC-AUC. Conclusion: The proposed framework achieved strong subject-wise performance and competitive cross-subject performance under a leakage-controlled evaluation protocol. Further refinement of false-positive control and prospective validation is needed before real-time clinical deployment.

Keywords:

Epileptic seizure detection,Electroencephalography (EEG) signals,Convolutional Neural Network,Vision Transformer,Adaptive Multi-Stream Feature Fusion,

References:

I. Chen, Wenna, et al. "An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy." BMC Medical informatics and Decision making 23.1 (2023): 96. 10.1186/s12911-023-02180-w
II. Christou, Vasileios, et al. "Evaluating the window size’s role in automatic EEG epilepsy detection." Sensors 22.23 (2022): 9233. 10.3390/s22239233
III. Ein Shoka, Athar A., et al. "EEG seizure detection: concepts, techniques, challenges, and future trends." Multimedia tools and applications 82.27 (2023): 42021-42051. 10.1007/s11042-023-15052-2
IV. Feng, Lufeng, et al. "A multi-view neural framework with attention for epileptic seizure classification." Journal of Neural Engineering 23.1 (2026): 016018. 10.1088/1741-2552/ae33f8
V. Guhdar, Mohammed, Ramadhan J. Mstafa, and Abdulhakeem O. Mohammed. "A multimodal temporal attention network for seizure classification via one-dimensional convolutional neural architecture." Biomedical Signal Processing and Control 112 (2026): 108495. 10.1016/j.bspc.2025.108495
VI. Islam, Md Rabiul, et al. "Epileptic seizure focus detection from interictal electroencephalogram: a survey." Cognitive neurodynamics 17.1 (2023): 1-23. 10.1007/s11571-022-09816-z
VII. Kashefi Amiri, Homa, Masoud Zarei, and Mohammad Reza Daliri. "Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform." Scientific Reports 15.1 (2025): 32820. 10.1038/s41598-025-18479-9
VIII. Kode, Hepseeba, Khaled Elleithy, and Laiali Almazaydeh. "Epileptic seizure detection in EEG signals using machine learning and deep learning techniques." IEEE access 12 (2024): 80657-80668. 10.1109/ACCESS.2024.3409581
IX. Lebal, Abdelhamid, Abdelouahab Moussaoui, and Abdelmounaam Rezgui. "Epilepsy-Net: attention-based 1D-inception network model for epilepsy detection using one-channel and multi-channel EEG signals." Multimedia tools and applications 82.11 (2023): 17391-17413. 10.1007/s11042-022-13947-0
X. Li, Qi, Wei Cao, and Anyuan Zhang. "Multi-stream feature fusion of vision transformer and CNN for precise epileptic seizure detection from EEG signals." Journal of Translational Medicine 23.1 (2025): 871. 10.1186/s12967-025-06862-z
XI. Li, Yang, et al. "Automatic seizure detection using fully convolutional nested LSTM." International journal of neural systems 30.04 (2020): 2050019. 10.1142/S0129065720500197
XII. Luo, Weitao, et al. "EEG-Based Brain-Computer Interface: Fundamentals, Methods, Applications, and Challenges." IEEE Internet of Things Journal (2025). 10.1109/JIOT.2025.3625060
XIII. Ma, Mengnan, et al. "Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN." BMC Medical Informatics and Decision Making 21.Suppl 2 (2021): 100. 10.1186/s12911-021-01438-5
XIV. Pan, Yayan, et al. "Downsampling of EEG signals for deep learning-based epilepsy detection." IEEE Sensors Letters 7.12 (2023): 1-4. 10.1109/LSENS.2023.3332392
XV. Ren, Juntao, et al. "An Event-Based Filtering and Weighted Enhanced Deep Learning Epileptic Seizure Prediction Method." Neural Networks (2025): 108424. 10.1016/j.neunet.2025.108424

XVI. Salini, G. Indu, I. Sowmy, and T. K. Sreeja. "FrAdadelta-CSA: Fractional Adadelta Chameleon Swarm Algorithm-based feature selection with SpikeGoogle-DenseNet for epileptic seizure detection." Computational Biology and Chemistry 119 (2025): 108550. 10.1016/j.compbiolchem.2025.108550
XVII. Shah, Syed Yaseen, et al. "Epileptic seizure classification based on random neural networks using discrete wavelet transform for electroencephalogram signal decomposition." Applied Sciences 14.2 (2024): 599. 10.3390/app14020599
XVIII. Shanmugam, Shalini, and Selvathi Dharmar. "A CNN-LSTM hybrid network for automatic seizure detection in EEG signals." Neural Computing and Applications 35.28 (2023): 20605-20617. 10.1007/s00521-023-08832-2
XIX. Srinivasan, Saravanan, et al. "Detection and classification of adult epilepsy using hybrid deep learning approach." Scientific reports 13.1 (2023): 17574. 10.1038/s41598-023-44763-7
XX. Sunaryono, Dwi, Riyanarto Sarno, and Joko Siswantoro. "Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features." Journal of King Saud University-Computer and Information Sciences 34.10 (2022): 9591-9607. 10.1016/j.jksuci.2021.11.015
XXI. Tasci, Irem, et al. "Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals." Information Fusion 96 (2023): 252-268. 10.1016/j.inffus.2023.03.022
XXII. Tawhid, Md Nurul Ahad, Siuly Siuly, and Tianning Li. "A convolutional long short-term memory-based neural network for epilepsy detection from EEG." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1-11. 10.1109/TIM.2022.3217515
XXIII. Wang, Quanhong, et al. "A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signals." Neurocomputing 562 (2023): 126874. 10.1016/j.neucom.2023.126874
XXIV. Wang, Zhuohan, et al. "EEG-based seizure detection using dual-branch CNN-ViT network integrating phase and power spectrograms." Brain sciences 15.5 (2025): 509. 10.3390/brainsci15050509
XXV. Xu, Gaowei, et al. "A one-dimensional CNN-LSTM model for epileptic seizure recognition using EEG signal analysis." Frontiers in neuroscience 14 (2020): 578126. 10.3389/fnins.2020.578126

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Abstract:

The rapid proliferation of large-scale Internet of Things (IoT) systems has imposed stringent requirements on Message Queuing Telemetry Transport (MQTT) infrastructures for scalability, security, and trust management. This research proposes a Hierarchical Trust-Oriented Broker Federation (HTBF) framework with fine-grained security enforcement to enable secure and elastic MQTT architectures across distributed edge–cloud environments. The proposed architecture organizes MQTT brokers into a three-tier hierarchy (edge, regional, and core layers), where inter-broker communication is governed by a dynamic trust evaluation model based on behavioral reliability, authentication success rate, and traffic anomaly scores. A lightweight trust computation function based on a discounted Bayesian state-space model enables real-time trust adaptation with negligible computational overhead (<2.1 ms per update). Fine-grained security policies are enforced using Attribute-Based Access Control (ABAC) combined with topic-level authorization, enabling per-client, per-topic, and per-payload security decisions. Experimental evaluation was conducted on a federated testbed comprising 30 brokers and 10,000 concurrent MQTT clients, deployed across edge and cloud nodes. Results demonstrate that the proposed HTBF model achieves a 43.7% reduction in unauthorized message propagation, a 31.2% improvement in broker resilience under coordinated attack scenarios, and a 27.5% decrease in average message latency compared to flat broker federation. Under high-load conditions (100,000 messages/s), the system maintained a throughput of 92,400 messages/s, with an average end-to-end latency of 18.6 ms and packet loss below 0.8%. Additionally, trust-based routing reduced malicious broker participation by 48.3%, significantly improving overall system reliability.

Keywords:

Hierarchical broker federation,Message Queuing Telemetry Transport,Trust management,Attribute-Based Access Control,IoT,Broker trust evaluation,,Access control policies,Message routing,Distributed systems security.,

References:

I. Agarwal, Sheetal, and Rupal Gupta. “Edge Computing for Energy Efficient IoT.” Energy Efficient Internet of Things?Based Wireless Sensor Network (2026): 187-215. 10.1002/9781394314751. ch7
II. Akshatha, P. S., and SM Dilip Kumar. “MQTT and blockchain sharding: An approach to user-controlled data access with improved security and efficiency.” Blockchain: Research and Applications 4.4 (2023): 100158. 10.1016/j.bcra.2023.100158
III. Al Hanif, Abdulelah, and Mohammad Ilyas. “Effective feature engineering framework for securing MQTT protocol in IoT environments.” Sensors 24.6 (2024): 1782. 10.3390/s24061782
IV. Allaga, Hamza, Mohamed Biniz, and Abderrazak Farchane. “MQTTEEB-D: A high-fidelity benchmark for real-time MQTT anomaly detection using machine learning techniques.” Ad Hoc Networks (2025): 104062. 10.1016/j.adhoc.2025.104062
V. Alqazzaz, Ali. “SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks.” Scientific Reports (2026). 10.1109/ICISS67859.2026.11453976
VI. Azzedin, Farag, and Turki Alhazmi. “Secure data distribution architecture in IoT using MQTT.” Applied Sciences 13.4 (2023): 2515. 10.3390/app13042515
VII. Chen, Ran, et al. “Blockchain-based MQTT communication access control scheme for the Internet of Things.” Second International Conference on Electronic Information Technology (EIT 2023). Vol. 12719. SPIE, 2023. 10.1117/12.2685781
VIII. Dhokane, Nilima Tatyasaheb, et al. “S-MQTT: A Secure MQTT Protocol with Merkle Tree Authentication and AES Encryption for IoT Communication Systems.” Ingenierie des Systemes d'Information 30.8 (2025): 1963. 10.18280/isi.300803
IX. Kamoun-Abid, Ferdaous, and Amel Meddeb-Makhlouf. “Enhanced MQTT Protocol for Securing Big Data/Hadoop Data Management.” Journal of Sensor and Actuator Networks 15.1 (2026): 22. 10.3390/jsan15010022
X. Ko, Kyeong Il, and Meong Hun Lee. “MQTT-Based Architecture for Real-Time Data Collection and Anomaly Detection in Smart Livestock Housing.” Sensors 25.23 (2025): 7186.
10.1109/HealthCom60686.2025.11343673
XI. Kurdi, Hassan, and Vijey Thayananthan. “A multi-tier MQTT architecture with multiple brokers based on fog computing for securing industrial IoT.” Applied Sciences 12.14 (2022): 7173. 10.3390/app12147173
XII. Maawi, Kholoud Nasser Al, and Munir Abdullah Abduh Qa'id. “A Review on Intrusion Detection Systems for MQTT in IoT Environments.” International Journal of Safety & Security Engineering 15.8 (2025). 10.18280/ijsse.150818
XIII. Radwan, Nael M., and Frederick T. Sheldon. “Experimental Evaluation of MQTT Authentication Mechanisms: Reliability, Enforcement Accuracy, and Security Implications.” (2026). 10.3390/app16073583
XIV. Thanh Binh, Bui Ngoc, et al. “A Protocol-Aware P4 Pipeline for MQTT Security and Anomaly Mitigation in Edge IoT Systems.” arXiv e-prints (2026): arXiv-2601. 10.48550/arXiv.2601.07536
XV. Wang, Ziang, et al. “Research on the Development of a Building Model Management System Integrating MQTT Sensing.” Sensors 25.19 (2025): 6069. 10.3390/s25196069

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Abstract:

This paper presents the design, simulation, fabrication, and experimental validation of an etched crescent-shaped microstrip patch antenna for compact multiband wireless communication and radio frequency energy harvesting (RFEH) applications. Through a systematic design evolution process, six antenna configurations are investigated. Two of these were fabricated. Design 5 represents the optimum configuration and exhibits the best overall electromagnetic performance among all investigated designs. It achieves excellent impedance matching with a minimum fabricated reflection coefficient of (-31 dB, -20dB, and -30dB) at (1.9, 4.2, and 5.2) GHz, respectively, and VSWR close to 1. This design demonstrates a peak gain of approximately 5.1 dBi and very high radiation and total efficiencies exceeding 93%, indicating efficient power transfer and low loss despite the use of an FR-4 substrate with dimensions of 60 × 40 × 1.6 mm³, employing a partial ground plane and a 50-? microstrip feed line. The final etched configuration (Design 6) is fabricated on a low-cost FR-4. The system uses a dual-band operating frequency for testing, with -20 decibel reflection coefficients between 1.90 and 5.10 gigahertz and over 80% radiation efficiency; the highest realized gain is approximately 4.70 dBi and exhibits nearly omnidirectional radiation characteristics. Design number five was able to achieve superior performance in all performance categories when compared to four other designs, making it the best candidate to provide a low-profile and cost-effective antenna for compact multiband wireless and RF energy harvesting systems. The results of all tests conducted on the proposed antenna show similar performance characteristics between simulated and tested, with only minor differences attributable to manufacturing tolerances or the measurement being performed. Based on current information, the proposed antenna has proven to provide a viable solution for future low-cost compact and efficient multiband Wireless and RF Energy Harvesting Systems.

Keywords:

crescent-shaped antenna,microstrip patch antenna,etched radiator,multiband antenna,RF energy harvesting,antenna fabrication,measurement,VNA,return loss.,

References:

I. Aafizaa, K., Uma Haimavathi, K., & Saravanan, S. (2026). Recent Innovations in Microstrip Patch Antennas: Biomedical Uses and Wireless Integration. Biomedical Materials & Devices, 4(1), 326-341.
II. Abbas, R. A., & Kadhum, M. H. (2024). A Review of Energy Harvesting Techniques for Self-Powered IoT Devices. Wasit Journal of Engineering Sciences, 12(4), 133-145.
III. Ahmad, I., Tan, W., Ali, Q., & Sun, H. (2022). Latest performance improvement strategies and techniques used in 5G antenna designing technology, a comprehensive study. Micromachines, 13(5), 717.
IV. Almawlawe, M. D. H., Al-Araji, Z., & Saitkulov, V. (2025). Impact of Substrate Dielectric Constant on Performance of 2.4 GHz Microstrip Patch Antenna Array. Wasit Journal of Engineering Sciences, 13(1), 22-38.

V. Aras, U., Delwar, T. S., Durgaprasadarao, P., Sundar, P. S., Ahammad, S. H., Eid, M. M., Lee, Y., Zaki Rashed, A. N., & Ryu, J.-Y. (2024). Dual features, compact dimensions and X-band applications for the design and fabrication of annular circular ring-based crescent-moon-shaped microstrip patch antenna. Micromachines, 15(7), 809.
VI. Arnaoutoglou, D. G., Empliouk, T. M., Kaifas, T. N., Chryssomallis, M. T., & Kyriacou, G. (2024). A review of multifunctional antenna designs for internet of things. Electronics, 13(16), 3200.
VII. Babu, G. H., Srinivas, M., Gnanaprakasam, C., Prabu, R. T., Devi, M. R., Ahammad, S. H., Hossain, M. A., & Rashed, A. N. Z. (2023). Meander line base asymmetric co-planar wave guide (CPW) feed tri-mode antenna for Wi-Max, North American Public Safety and satellite applications. Plasmonics, 18(3), 1007-1018.
VIII. Dadhich, A., Samdani, P., Deegwal, J., & Sharma, M. (2019). Design and investigations of multiband microstrip patch antenna for wireless applications. In Ambient Communications and Computer Systems: RACCCS-2018 (pp. 37-45). Springer.
IX. Gatea, Q. M., & Ali, F. M. (2025). Design and Implementation of High-Gain Wide-Bandwidth Patch Antenna Array 5G Base Stations. Wasit Journal of Engineering Sciences, 13(3), 51-61.
X. Ghorbani, A., Ansarizadeh, M., & Abd-Alhameed, R. (2009). Bandwidth limitations on linearly polarized microstrip antennas. IEEE Transactions on Antennas and Propagation, 58(2), 250-257.
XI. Guha, D., Kumar, C., & Biswas, S. (2022). Defected ground structure (DGS) based antennas: design physics, engineering, and applications. John Wiley & Sons.
XII. Husien, N. Q. A., & Al-khazaali, H. F. K. (2024). Design of Microstrip Antenna Array for Autonomous Vehicles. Wasit Journal of Engineering Sciences, 12(4), 103-112.
XIII. Ibrahim, H. H., Singh, M. J., Al-Bawri, S. S., Ibrahim, S. K., Islam, M. T., Alzamil, A., & Islam, M. S. (2022). Radio frequency energy harvesting technologies: A comprehensive review on designing, methodologies, and potential applications. Sensors, 22(11), 4144.
XIV. Isa, S. R., Jusoh, M., Sabapathy, T., Nebhen, J., Kamarudin, M. R., Osman, M. N., Abbasi, Q. H., Rahim, H. A., & Yasin, M. N. M. (2022). Reconfigurable Pattern Patch Antenna for Mid-Band 5G: A Review. Computers, Materials & Continua, 70(2).
XV. Kaim, V., Singh, N., Kanaujia, B. K., Matekovits, L., Esselle, K. P., & Rambabu, K. (2022). Multi-channel implantable cubic rectenna MIMO system with CP diversity in orthogonal space for enhanced wireless power transfer in biotelemetry. IEEE Transactions on Antennas and Propagation, 71(1), 200-214.
XVI. Khandelwal, M. K., Kanaujia, B. K., & Kumar, S. (2017). Defected ground structure: fundamentals, analysis, and applications in modern wireless trends. International Journal of antennas and Propagation, 2017(1), 2018527.
XVII. Mishra, B., Verma, R. K., & Singh, R. K. (2022). A review on microstrip patch antenna parameters of different geometry and bandwidth enhancement techniques. International Journal of Microwave and Wireless Technologies, 14(5), 652-673.
XVIII. Quan, L., Zhong, X., Liu, X., Gong, X., & Johnson, P. A. (2014). Effective impedance boundary optimization and its contribution to dipole radiation and radiation pattern control. Nature communications, 5(1), 3188.
XIX. Räsänen, M. (2023). Design and analysis of a high-gain and wide-band phased-array antenna for V-band.
XX. Ren, Y., Luo, W., He, Z., Qin, N., Meng, Q., Qiu, M., Li, J., Yang, H., Xu, L., & Li, Y. (2025). Development and performance study of a radiation-enhanced heat pipe radiator for cooling high-power IGBT modules. Applied Thermal Engineering, 262, 125307.
XXI. Sabban, A. (2022). Wearable circular polarized antennas for health care, 5G, energy harvesting, and IoT systems. Electronics, 11(3), 427.
XXII. Sabban, A. (2024). Green wearable sensors and antennas for bio-medicine, green internet of things, energy harvesting, and communication systems. Sensors, 24(17), 5459.
XXIII. Sharma, V. (2020). Microstrip antenna-inception, progress and current-state of the art review. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 13(6), 769-794.
XXIV. Suganya, E., Pushpa, T. A. J. M., & Prabhu, T. (2024). Advancements in patch antenna design for Sub-6 GHz 5G smartphone application: a comprehensive review. Wireless personal communications, 137(4), 2217-2252.
XXV. Suryapaga, V., & Khairnar, V. V. (2024). Review on multifunctional pattern and polarization reconfigurable antennas. IEEE Access, 12, 90218-90251.
XXVI. Zainud-Deen, S. H., El-Shalaby, N. A., Malhat, H. A., & Gaber, S. M. (2019). Reconfigurable multi-turns planar plasma helical antenna. Plasmonics, 14(6), 1831-1837.

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Abstract:

This paper presents a comprehensive examination of anti-fuzzy implicative and anti-fuzzy sub-implicative ideals in Z-algebras. Drawing from recent advances in algebraic fuzzy logic, we investigate the fundamental properties, structural relationships, and applications of these novel ideal concepts. We establish the interrelationships between fuzzy Z-ideals, fuzzy implicative ideals, and their anti-fuzzy counterparts, demonstrating that anti-fuzzy structures provide complementary frameworks for modelling uncertainty in non-associative algebraic systems. Key theoretical results include characterization theorems, preservation properties under homomorphisms, and conditions for equivalence between different ideal classes. Crucially, we provide formal derivations showing that the implicative condition implies the medial condition using Z-algebra axioms, and we verify the well-definedness of the supremum-based image construction under surjective homomorphisms. This research contributes to the broader understanding of how fuzzy and anti-fuzzy methodologies can coexist in abstract algebra to address limitations in classical ideal theory.

Keywords:

Z-algebras,fuzzy ideals,anti-fuzzy ideals,implicative ideals,medial condition,fuzzy logic,algebraic uncertainty,homomorphisms,

References:

I. Chandramouleeswaran, M., P. Muralikrishna, K. Sujatha, and S. Sabarinathan. “A Note on Z-Algebras.” Italian Journal of Pure and Applied Mathematics 38 (2017): 707–714. https://ijpam.uniud.it/online_issue/201738/61.pdf
II. Imai, Y., and K. Iseki. “On Axiom Systems of Propositional Calculi XIV.” Proceedings of the Japan Academy 42 (1966): 19–22. 10.3792/pja/1195522169
III. Iseki, K. “An Algebra Related with a Propositional Calculus.” Proceedings of the Japan Academy 42 (1966): 26–29. 10.3792/pja/1195522171
IV. Jun, Y. B., E. H. Roh, and S. M. Mostafa. “On Fuzzy Implicative Ideals of BCK-Algebras.” Soochow Journal of Mathematics 25.1 (1999): 57–70.
V. Meng, J., and X. L. Xin. “Implicative BCI-Algebras.” Pure and Applied Mathematics 8.2 (1992): 99–103.
VI. Mounikalakshmi, R. “Implicative and Positive Implicative INK-Ideals.” European Journal of Pure and Applied Mathematics 18.1 (2025): 123–145. 10.29020/nybg.ejpam.v18i1.5678
VII. Oner, T. “(Anti) Fuzzy Ideals of Sheffer Stroke BCK-Algebras.” Advances in Algebraic Structures. Academic Publishers, 2023. 234–245.
VIII. Sowmiya, S., and P. Jeyalakshmi. “On Fuzzy Implicative Ideals in Z-Algebras.” Advances and Applications in Mathematical Sciences 21.10 (2022): 5911–5922.
IX. Xi, O. G. “Fuzzy BCK-Algebras.” Mathematica Japonica 36.5 (1991): 935–942.
X. Zadeh, L. A. “Fuzzy Sets.” Information and Control 8.3 (1965): 338–353. 10.1016/S0019-9958(65)90241-X

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Abstract:

Aditya Route Rover: Low-Cost and Efficient Bus Monitoring System is an integrated system that offers real-time monitoring of college buses with emphasis on the safety of the students and the efficiency of the buses. The proposed system is a combination of GPS technology and face recognition, which is used to continually monitor the position of the bus and students on board. Processing of captured digital images and video frames is used to extract facial features for real-time student recognition. The GPS module can be relied on to give the correct location of the students, and the face recognition module will help to monitor students securely and reliably. An onboard controller processes the collected information from the sensors and communicates to a central server, where it is accessed and managed in real-time. The system increases transparency in the transportation of college students for safety, and it offers reassurance to parents. The proposed system can be deployed in academic transportation because of its low-cost structure and scalable architecture

Keywords:

GPS module,Face Recognition,Raspberry Pi,Real-time monitoring,College Bus,Smart Transportation.,

References:

I. Ashraf, Muhammad Hassaan, et al. “HVD-Net: A Hybrid Vehicle Detection Network for Vision-Based Vehicle Tracking and Speed Estimation.” Journal of King Saud University – Computer and Information Sciences, vol. 35, no. 8, The Authors, 2023, pp. 1–19, 10.1016/j.jksuci.2023.101657.
II. Atzori, Luigi, et al. “The Internet of Things: A Survey.” Computer Networks, vol. 54, no. 15, 2010, pp. 2787–805.
III. Balani, Zina, and Mohammed Nasseh Mohammed. “Web-Based Bus Tracking System in the Internet of Things IoT.” International Journal of Science and Business, vol. 28, no. 1, 2023, pp. 31–40, 10.58970/ijsb.2203.
IV. Bharte, V., et al. “Bus Monitoring System Using Polyline Algorithm.” International Journal of Scientific and Research Publications, vol. 4, no. 4, 2014, pp. 1–4, http://www.ijsrp.org/research-paper-0414/ijsrp-p2829.pdf.
V. Botta, Alessio, et al. “Integration of Cloud Computing and Internet of Things: A Survey.” Future Generation Computer Systems, vol. 56, 2016, pp. 684–700.
VI. Huynh, Nguyen Bao Phuong, and Duy Thong Nguyen. “Implementation of an IoT-Based System for Monitoring Parameters and Tracking Transport Vehicles.” Journal of Technical Education Science, vol. 19, no. 06, 2024, pp. 66–74.
VII. Jain, A. K., et al. “An Introduction to Biometric Recognition.” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, 2004, pp. 4–20.
VIII. Jain, Anjali, and Dr. Agya Mishra. “Design of IoT Based Real-Time Bus Tracking App Using HF-RFID.” The International Journal of Recent Technology and Engineering (IJRTE), vol. 9, no. 6, 2021, pp. 71–75, 10.35940/ijrte.f5365.039621.
IX. Jeyakkannan, N., et al. “IoT Based Smart Bus System Using Wireless Sensor Networks.” Journal of Physics: Conference Series, vol. 1937, no. 1, 2021, pp. 1–9. 10.1088/1742-6596/1937/1/012017.
X. Krishnan, R. Santhana, et al. “Secured College Bus Management System Using IoT for Covid-19 Pandemic Situation.” Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 2021, pp. 376–82. 10.1109/ICICV50876.2021.9388378.
XI. Kulkarni, Apeksha P., and Vishwanath P. Baligar. “Real Time Vehicle Detection, Tracking and Counting Using Raspberry-Pi.” 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 – Conference Proceedings, no. Icimia, 2020, pp. 603–07. 10.1109/ICIMIA48430.2020.9074944.
XII. Lago, Allan, et al. “Low-Cost Real-Time Aerial Object Detection and GPS Location Tracking Pipeline.” ISPRS Open Journal of Photogrammetry and Remote Sensing, vol. 13, no. May, Elsevier B.V., 2024, pp. 1–8. 10.1016/j.ophoto.2024.100069.
XIII. Maruthi, R., and C. Jayakumari. “SMS Based Bus Tracking System Using Open Source Technologies.” International Journal of Computer Applications, vol. 86, no. 9, 2014, pp. 44–46. 10.5120/15017-3305.
XIV. McCarthy, Chris, et al. “A Field Study of Internet of Things-Based Solutions for Automatic Passenger Counting.” IEEE Open Journal of Intelligent Transportation Systems, vol. 2, no. January, IEEE, 2021, pp. 384–401. 10.1109/OJITS.2021.3111052.
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Abstract:

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

Keywords:

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

References:

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

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

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

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

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

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

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

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

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

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

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

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

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Abstract:

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

Keywords:

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

References:

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

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Abstract:

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

Keywords:

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

References:

I. Altman, Douglas G. Practical statistics for medical research. Chapman and Hall/CRC, 1990. 10.1201/9780429258589
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III. Chang, Yao-Jen, et al. "Recursive partitioning analysis of lymph node ratio in breast cancer patients." Medicine 94.1 (2015): e208. 10.1097/MD.0000000000000208
IV. Collett, David. Modelling survival data in medical research. Chapman and Hall/CRC, 2023. 10.1201/9781003282525
V. Cox, David R. "Regression models and life?tables." Journal of the royal statistical society: Series B (methodological) 34.2 (1972): 187-202. 10.1007/978-1-4612-4380-9_37

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

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XXV. Wei, Lee-Jen. "The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis." Statistics in medicine 11.14?15 (1992): 1871-1879. 10.1002/sim.4780111409
XXVI. Wienke, Andreas. Frailty models in survival analysis. Chapman and Hall/CRC, 2010. 10.1201/9781420073911
XXVII. Yazdani, Akram, et al. "Application of Frailty Quantile Regression Model to investigate of factors survival time in breast Cancer: a Multi-center Study." Health Services Research and Managerial Epidemiology 10 (2023): 23333928231161951. 10.1177/23333928231161951

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Abstract:

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

Keywords:

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

References:

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

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Abstract:

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

Keywords:

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

References:

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VIII. Kurniawan, Latief Anggar, and F. Farizal. "Development of Flow Shop Scheduling Method to Minimize Makespan Based on Nawaz Enscore Ham (NEH) and Campbell Dudek and Smith (CDS) Method." Proceedings of the 3rd African International Conference on Industrial Engineering and Operations Management, 2022, pp. 1224-31. 10.46254/AF03.20220230.
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XIV. Ruiz, Rubén, and José Antonio Vázquez-Rodríguez. "The Hybrid Flow Shop Scheduling Problem." European Journal of Operational Research, vol. 205, no. 1, 2010, pp. 1-18. 10.1016/j.ejor.2009.09.024.
XV. Senapati, Tapan, et al. "Analysis of Interval-Valued Intuitionistic Fuzzy Aczel–Alsina Geometric Aggregation Operators and Their Application to Multiple Attribute Decision-Making." Axioms, vol. 11, no. 6, 2022, pp. 258. 10.3390/axioms11060258.
XVI. Xu, Ze Shui. "Methods for Aggregating Interval-Valued Intuitionistic Fuzzy Information and Their Application to Decision Making." Control and Decision, vol. 22, no. 2, 2007, pp. 215-19. https://www.researchgate.net/publication/271843255_Methods_for_aggregating_intervalvalued_intuitionistic_fuzzy_information_and_their_application_to_decision_making

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Abstract:

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

Keywords:

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

References:

I. Brand, J.S., and Y. Tang. "What can we learn about mental health from patient narratives using a novel quantitative–qualitative network analysis?" Network Science, vol. 12, no. 4, 2024, pp. 331-8.
II. Briganti, G., and P. Linkowski. "Network analysis: An overview for mental health research." International Journal of Methods in Psychiatric Research, vol. 33, no. 4, 2024, e2034.
III. Cai, H., et al. "A network model of depressive and anxiety symptoms: a statistical evaluation." Molecular Psychiatry, vol. 29, no. 3, 2024, pp. 767-81.
IV. Castro, I. D., et al. "Centrality measures in psychological networks: A simulation study on identifying effective treatment targets." PLoS One, vol. 19, no. 2, 2024, e0297058.
V. Fan, W., et al. "Uncovering the complex interactions of mental health symptoms in Chinese college students: Insights from network analysis." BMC Psychology, vol. 13, 2025, p. 448.
VI. Fried, E.I., and R.M. Nesse. "Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study." Journal of Affective Disorders, vol. 172, 2015, pp. 96-102.
VII. Odenthal, M., et al. "Temporal dynamics in mental health symptoms and loneliness during the COVID-19 pandemic in a longitudinal probability sample: A network analysis." Translational Psychiatry, vol. 13, 2023, p. 162.
VIII. Oliver, D., et al. "Longitudinal evolution of the transdiagnostic prodrome to severe mental disorders: A dynamic temporal network analysis informed by natural language processing and electronic health records." Molecular Psychiatry, 2025.
IX. Robinaugh, D.J., et al. "The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research." Psychological Medicine, vol. 50, no. 3, 2020, pp. 353-66.
X. Schumacher, L.D., et al. "Dynamic changes in network structure of depressive symptoms: A two-year naturalistic follow-up study." BMC Psychiatry, vol. 24, 2024, p. 192.

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