Journal Vol – 21 No – 6, June 2026

Abstract:

This work uses the eight-noded quadrilateral finite element method to study the eigen analysis for several energy issues in the multiply-connected domain. The computing of eigenvalues across circular and multiply-connected curved domains is one of its applications, this method makes advantage of an excellent, eight-noded, quadrilateral automatic mesh generator created with MAPLE-18 software, this method makes use of a superb FEM process as seen by the examples provided, the proposed technique provides efficient numerical solutions for a range of problems and increases the accuracy of the numerical solution of eigenvalues that occur in a number of electromagnetic applications due to the reduced curvature loss, the validity of the current concept is demonstrated by these issues, the numerical results for the example situations using the suggested approach are quite similar to the best-published results.

Keywords:

Eight-node,Mesh,Multi-connected regions,FEM,Helmoltz equation,

References:

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Abstract:

Federated Learning (FL) is a type of distributed learning where several clients (clusters) train a machine learning model without sharing the raw data directly. Nevertheless, there are still three significant issues for practical FL systems: privacy leakage through sharing the model update, heavy communication burden, and unstable learning performance when the data distribution is not IID. To enhance the protection of privacy and communication efficiency in distributed big data analytics, the authors introduce a Privacy-Preserving Federated Learning (PP-FL) framework combining Differential Privacy (DP), Secure Aggregation (SA), and Adaptive Gradient Compression (AGC). Differential Privacy implies that calibrated Gaussian noise is added to local updates, and Secure Aggregation ensures that the central entity cannot see individual updates from the clients. Adaptive Gradient Compression cuts down on communicating the most important components of a gradient. The proposed framework is tested with the high-resolution MNIST dataset and CIFAR-10 dataset in non-IID federated settings. The experimental results demonstrate that PP-FL has a significant boost in reducing communication cost compared to standard FedAvg and DP-FedAvg. The results also illustrate a clear trade-off between privacy and utility, as the addition of noise (differential privacy) or departure from an IID data distribution or extreme compression can negatively impact classification accuracy. This behaviour is explained by means of revised studies that consist of a component-wise interpretation, observation of the gradient norm, and evaluation of the compression ratio. The overall results suggest that the proposed PP-FL system can be employed to implement communication-efficient and privacy-preserving federated learning, and that careful tuning of noise parameters and compression parameters is essential to ensure the stability of learning.

Keywords:

Federated Learning,Differential Privacy,Secure Aggregation,Privacy-Preserving Machine Learning,Gradient Compression,Distributed Learning,Big Data Analytics,

References:

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Abstract:

Localization of sensors in an indoor wireless sensor network (WSNs) has been a very difficult task because of attenuation of signals, multipath, and also a low supply of anchors. In this paper, we propose a new hybrid acoustic-radio system, Collaborative Acoustic Echo Mapping (CAEM), to achieve high-precision localization of sensors within an indoor environment. The proposed method combines acoustic echo data from environmental reflectors with radio-based inter-sensor ranging data, enabling simultaneous optimization of sensor and reflector locations. It considers a robust formulation based on the Huber loss to reduce the effects of measurement noise and outliers. It solves the resulting non-linear optimization problem using an efficient L-BFGS-B scheme. Extensive simulations are conducted in a 100 m x 100 m indoor space at different noise levels, anchor densities, and sensor locations. The Proposed CAEM model is compared with eight standard and contemporary strategies. Findings indicate that CAEM consistently outperforms traditional methods, minimizing localization error by up to 3.2 times. In a representative scenario, CAEM achieves an RMSE of 11.33 m, which is far better than the baselines. The results emphasise the utility of the acoustic and radio modalities approach for robust, scalable indoor localisation, making CAEM a promising solution for next-generation IoT and WSN applications.

Keywords:

Collaborative Acoustic Echo Mapping (CAEM),Indoor Localization,Wireless Sensor Networks,Acoustic–Radio Fusion,Non-linear Optimization,Multi-modal Sensor Fusion,

References:

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Abstract:

Objective: The investigation of a nonlocal elastic hollow cylinder with double porosity has been presented for rigidly fixed boundary conditions in the context of generalized thermoelasticity. Governing equations are transformed into ordinary differential equations through harmonics variation technique. Methods: The system of equations is solved with the help of the matrix elimination approach, which yields a characteristic equation of eighth degree. The unknown field functions for dilatation, porosity, temperature, and displacement have been shown analytically. Analytical results are verified through numerical simulations by Computer based MATLAB software by using Iteration numerical technique. Generated data through simulations corresponds to the roots of the equation, termed the mode number. Results: The generated data is of the form complex numbers, which reveals that the real part is known as the natural frequency, and the imaginary part represents the damping factor. The computer analysed and generated data has been presented graphically for frequency shift, thermoelastic damping, and field functions such as displacement, porosity, and temperature. The generated data has also been shown in tables for natural frequencies. Conclusions: The findings of the study have potential relevance in smart materials, nanotechnology, biomedical implants, energy storage systems, and aerospace components.

Keywords:

Porosity,Thermoelasticity,Free vibrations,Natural frequency,Frequency shift,Rigidly fixed boundaries.,

References:

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Abstract:

Imbalanced datasets present significant challenges for standard classification algorithms and often lead to biased models that perform poorly on minority classes. To address this, this study proposes a framework combining adaptive data transformation, Multi-class Mahalanobis Distance (MMD) metric learning, and logistic regression. The MMD transformation optimizes the feature space by computing a shared covariance matrix to pull similar data points closer and to increase class separability. The proposed MMD (LR) method significantly outperformed existing techniques like Local Mahalanobis Distance Learning (LMDL) on various benchmark imbalanced datasets. MMD(LR) achieved an average performance gain of 6.70% in precision, 7.16% in F1-score, and 14.10% in Area Under the Curve (AUC). Notably, the model achieved a perfect 100% AUC on the Wine and Iris datasets, and a 99.69% AUC on the Breast Cancer dataset. It demonstrates its exceptional robustness and adaptability for classifying complex and imbalanced data.

Keywords:

Classification,Imbalanced dataset,Logistic Regression,Machine Learning,Mahalanbois distance,Multivariate datasets.,

References:

I. Aeberhard, S., and M. Forina. Wine Dataset. UCI Machine Learning Repository, 1992. 10.24432/C5PC7J.
II. Altalhan, M., A. Algarni, and M. Turki-Hadj Alouane. “Imbalanced Data Problem in Machine Learning: A Review.” IEEE Access, 2025. 10.1109/access.2025.3531662.
III. Shrivastava, A., and P. R. Vamsi. “Improving Anomaly Classification using Combined Data Transformation and Machine Learning Methods.” International Journal of Performability Engineering, vol. 20, 2024, p. 68. 10.23940/ijpe.24.02.p2.6880.
IV. Ashoorzadeh, A., A. T. Eshlaghy, and M. A. Kazemi. “A Novel Classification Method: A Hybrid Approach Based on Large Margin Nearest Neighbor Classifier.” Journal of Computational Robotics, vol. 17, 2024, pp. 17–34.
V. Blum, L., M. Elgendi, and C. Menon. “Impact of Box-Cox Transformation on Machine-Learning Algorithms.” Frontiers in Artificial Intelligence, vol. 5, 2022. 10.3389/frai.2022.877569.
VI. Boudt, K., P. J. Rousseeuw, S. Vanduffel, and T. Verdonck. “The Minimum Regularized Covariance Determinant Estimator.” Statistics and Computing, vol. 30, 2019, pp. 113–128. 10.1007/s11222-019-09869-x.
VII. Chen, Y., A. Wiesel, Y. C. Eldar, and A. O. Hero. “Shrinkage Algorithms for MMSE Covariance Estimation.” vol. 58, 2010, pp. 5016–5029. 10.1109/TSP.2010.2053029.
VIII. Dai, D., J. Pan, and Y. Liang. “Regularized Estimation of the Mahalanobis Distance Based on Modified Cholesky Decomposition.” vol. 8, 2022, pp. 559–573. 10.1080/23737484.2022.2107961.
IX. de Amorim, L. B., G. D. Cavalcanti, and R. M. Cruz. “The Choice of Scaling Technique Matters for Classification Performance.” Applied Soft Computing, vol. 133, 2023, article 109924.
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XI. de Vazelhes, W., C. J. Carey, Y. Tang, N. Vauquier, and A. Bellet. “metric-learn: Metric Learning Algorithms in Python.” Journal of Machine Learning Research, vol. 21, 2020, pp. 1–6.
XII. Elmi, J., M. Eftekhari, A. Mehrpooya, and M. R. Ravari. “A Novel Framework Based on the Multi Label Classification for Dynamic Selection of Classifiers.” International Journal of Machine Learning and Cybernetics, vol. 14, 2023, pp. 2137–2154. 10.1007/s13042-022-01751-z.
XIII. Fisher, R. A. Iris Dataset. UCI Machine Learning Repository, 1936. 10.24432/C56C76.
XIV. Gao, X., D. Xie, Y. Zhang, et al. “A Comprehensive Survey on Imbalanced Data Learning.” arXiv, 2025, arXiv:2502.08960.
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XVI. Kahn, M. Diabetes Dataset. UCI Machine Learning Repository, 1990, 10.24432/C5T59G.
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XXIII. Siddappa, N. G., and T. Kampalappa. “Imbalance Data Classification Using Local Mahalanobis Distance Learning Based on Nearest Neighbor.” SN Computer Science, vol. 1, 2020, article 76. 10.1007/s42979-020-0085-x.

XXIV. Suárez, J. L., S. García, and F. Herrera. “A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges.” Neurocomputing, vol. 425, 2021, pp. 300–322. 10.1016/j.neucom.2020.08.017.
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XXXI. Hu, Lianyu, et al. “Interpretable Clustering: A Survey.” ACM Computing Surveys, vol. 58, no. 8, 2026, pp. 1–21. 10.1145/3789495.

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Abstract:

SCAPS-1D has been used in this work to model and simulate Copper Tin Sulphide/Selenide (CTS/CTSe) based device numerically. The study focuses on the performance improvement of solar cells made of copper tin sulfide/selenium (CTS/CTSe). Nowadays,CTS/CTSe is labelled as a promising absorber layer, which is analogous to the Kesterite (CZTS) absorber layer. The output parameters are optimized against the variation in Material, Electrical, and Optical parameters. The assumed inputs employed in the numerical simulation are consistent with the practical values. The result demonstrates that by optimization of different layers in the cell, the maximum efficiency of CTS and CTSe obtained are 17.5% and 18.5%, respectively.

Keywords:

Numerical Modeling; SCAPS-1D; CTS; CTSe,

References:

I. Andersson, B. A. “Materials Availability for Large-Scale Thin-Film Photovoltaics.” Progress in Photovoltaics: Research and Applications, vol. 8, no. 1, 2000, pp. 61–76. 10.1002/(SICI)1099-159X(200001/02)8:1<61::AID-PIP301>3.0.CO;2-6.
II. Burgelman, Marc, Koen Decock, Alex Niemegeers, Johan Verschraegen, and Stefaan Degrave. SCAPS Manual. University of Gent, 2021. URL: https://scaps.elis.ugent.be/SCAPS%20manual%20most%20recent.pdf.
III. Chierchia, R., et al. “Properties of Copper Tin Sulphide Thin Films for Photovoltaic Applications.” Physica Status Solidi C, vol. 13, no. 1, 2016, pp. 35–40. 10.1002/pssc.201510214.
IV. Dubey, P. K., and V. V. Paranjape. “Open-Circuit Voltage of a Schottky-Barrier Solar Cell.” Journal of Applied Physics, vol. 48, 1977, pp. 324–328. 10.1063/1.323560.
V. Et-taya, L., T. Ouslimane, and A. Benami. “Numerical Analysis of Earth-Abundant Cu₂ZnSn(SxSe1−x)₄ Solar Cells Based on Spectroscopic Ellipsometry Results by Using SCAPS-1D.” Solar Energy, vol. 201, 2020, pp. 827–835. 10.1016/j.solener.2020.03.056.
VI. Fernandes, P., P. Salomé, and A. da Cunha. “Study of Cu₂SnS₃ Thin Films for Solar Cell Applications.” Journal of Physics D: Applied Physics, vol. 43, no. 21, 2010, p. 215403. 10.1088/0022-3727/43/21/215403.
VII. Green, Martin A., et al. “Solar Cell Efficiency Tables.” Progress in Photovoltaics: Research and Applications, vol. 27, no. 1, 2019. 10.1002/pip.3102.
VIII. Hossain, E. S., et al. “Copper Tin Sulphide Thin Films for Photovoltaic Applications.” Current Applied Physics, vol. 18, no. 1, 2018, pp. 79–89. 10.1016/j.cap.2017.11.001.
IX. Khoshsirat, N., and N. A. Md Yunus. “Numerical Simulation of CIGS Thin Film Solar Cells Using SCAPS-1D.” IEEE Conference on Sustainable Utilization and Development in Engineering and Technology, 2013. URL: https://ieeexplore.ieee.org/
X. Kuku, T. A., and O. A. Fakolujo. “Optical Properties of Copper Tin Sulphide Thin Films.” Solar Energy Materials, vol. 16, no. 1, 1987, pp. 199–205. URL: https://www.sciencedirect.com/
XI. Kamal, M. M., and Mia. “Optimization of Linear Antenna Array Thinning Using Binary Genetic Algorithm (BGA).” 2022 International Conference on Recent Trends in Information Technology (ICRITO), IEEE, 2022, 10.1109/ICRITO56286.2022.9964507.
XII. Pallavolu, Mohan Reddy, et al. “Status Review on Cu₂SnSe₃ (CTSe) Thin Films for Photovoltaic Applications.” Solar Energy Materials and Solar Cells, vol. 208, 2020. 10.1016/j.solmat.2020.110367.
XIII. Peijie, L., et al. “Numerical Simulation of Cu₂ZnSnS₄-Based Solar Cells with In₂S₃ Buffer Layers by SCAPS-1D.” Journal of Applied Science and Engineering, vol. 17, no. 4, 2014, pp. 383–390. URL: http://jase.tku.edu.tw/
XIV. Rahaman, Sabina, et al. “Effect of Copper Concentration on CTS Thin Films for Solar Cell Absorber Layer and Photocatalysis Applications.” Materials Science in Semiconductor Processing, vol. 145, 2020, Article 106589. 10.1016/j.mssp.2020.106589.
XV. Rahman, M. A. “Enhancing the Photovoltaic Performance of Cd-Free Cu₂ZnSnS₄ Heterojunction Solar Cells Using SnS HTL and TiO₂ ETL.” Solar Energy, vol. 215, 2021, pp. 64–76. 10.1016/j.solener.2020.12.058.
XVI. Rui, K., et al. “New World Record CIGSSe Thin-Film Solar Cell Efficiency Beyond 22%.” IEEE 43rd Photovoltaic Specialists Conference, 2016. URL: https://ieeexplore.ieee.org/
XVII. Sato, S., et al. “Properties of Cu₂SnSe₃ Thin Films for Photovoltaic Applications.” Physica Status Solidi C, vol. 12, no. 6, 2015, pp. 757–761. 10.1002/pssc.201510040.
XVIII. Schubert, B.-A., et al. “Advances in Thin-Film Photovoltaic Technologies.” Proceedings of the 23rd European Photovoltaic Solar Energy Conference, Valencia, 2008. URL: https://www.eupvsec-proceedings.com/

XIX. Simya, O. K., A. Mahaboobbatcha, and K. Balachander. “A Comparative Study on the Performance of Kesterite-Based Thin Film Solar Cells Using SCAPS Simulation Program.” Superlattices and Microstructures, vol. 82, 2015, pp. 248–261. 10.1016/j.spmi.2015.02.029.
XX. Zhao, W., W. Zhou, and X. S. Miao. “Numerical Simulation of CZTS Thin Film Solar Cell.” IEEE NEMS Conference, 2012. 10.1109/NEMS.2012.6196826.

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Abstract:

Decentralized Autonomous Organizations (DAOs) manage by leveraging distributed ledger technology to capable unfaithful adjustment among members, allowing organizational regulations and operations to be executed autonomously through verifiable and tamper-resistant code.DAOs seek to encourage open and associate ecosystems in which members allocate resources, allocate capital, and build collective judgments transparently through predetermined, programmable governance mechanisms. This study observes the theoretical bases of DAOs, examines their possible real-world applications, and analyzes the importance of open-source improvement in advancing decentralized organizational models. Despite their innovative potential, DAOs encounter sufficient barriers such as suspicious regulatory frameworks, restrictions in governance mechanisms, and safety weaknesses arising from dependence on smart contract code. By integrating scholarly literature, real-world case analyses, and present actualizations, this paper provides an extensive perspective on the developed influence of DAOs within digital and financial ecosystems while identifying promising ways for ongoing research. Although DAOs remain extensively experimental, they reflect a potential purpose to review and redesign organizational combination, cooperation, and governance exercises appropriate to the claims of the digital era.

Keywords:

Blockchain,Crypto-economics,Decentralized Autonomous Organizations,Decentralized Finance,Decentralized Governance,Distributed Ledger Technology,Smart Contracts,Trustless Collaboration,

References:

I. Hassan, S., De Filippi, P., & Reijers, W. : “Decentralized Autonomous Organization: The Automation of Bureaucracy”, In Journal of Business Ethics, DOI:10.1007/s10551-021-05083-5, 2021.
II. Reijers, W., & Coeckelbergh, M.: “The Blockchain as a Narrative Technology: Investigating the Social Ontology and Normativity of Blockchain Technologies”, 2018.
III. Xu, J., & Koivisto, J. : “A Systematic Literature Review of Decentralized Autonomous Organizations (DAOs)”, In Blockchain: Research and Applications, 2022.
IV. Ellinger, E. W., Mini, T., Gregory, R. W., & Dietz, A.: “Decentralized Autonomous Organization (DAO): The case of MakerDAO”, 2023.
V. Hsieh, Y.-Y., Vergne, J.-P., Anderson, P. et al.: “ Bitcoin and the rise of decentralized autonomous organizations.”, Journal of Organization Design, 2018.
VI. N. Augustin, A. Eckhardt, & A. de Jong.: “Understanding decentralized autonomous organizations from the inside.”, Electronic Markets ,2023.
VII. Ziegler, C. & Zehra, S.: “Decoding Decentralized Autonomous Organizations: A Content Analysis Approach to Understanding Scoring Platforms.”, Journal of Risk and Financial Management, 2023.

VIII. Santana, C., & Albareda, L.: “Blockchain and the emergence of Decentralized Autonomous Organizations (DAOs): An integrative model and research agenda.”, Technological Forecasting and Social Change , 2022.
IX. Liu, L., Zhou, S., Huang, H. & Zheng, Z.: “From Technology to Society: An Overview of Blockchain-based DAO.”,2020.
X. Faqir-Rhazoui, Y., Arroyo, J., & Hassan, S.: “A comparative analysis of the platforms for decentralized autonomous organizations in the Ethereum blockchain.”, Journal of Internet Services and Applications, 10.1186/s13174-021-00139-6, 2021.
XI. Hsieh, Y.-Y., Vergne, J.-P., & Anderson, P. : “ Bitcoin and the rise of decentralized autonomous organizations.”, Journal of Organization Design, 10.1186/s41469-018-0038-1, 2018.
XII. Chen, H., Pendleton, M., Njilla, L., & Xu, S.: “ A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses.”, https://arxiv.org/abs/1908.04507, 2020.
XIII. Ziegler, C. & Zehra, S. : “ Decoding Decentralized Autonomous Organizations: A Content Analysis Approach to Understanding Scoring Platforms.”, Journal of Risk and Financial Management, 10.3390/jrfm16070330, 2023.
XIV. Pereira, J. R., & Garcia, G.: “DAOs: Governance in the Blockchain Era.”, https://www.intechopen.com/chapters/86578, 2023.
XV. Wang, Q., Liu, Y., & Liu, Q. : “ An Empirical Study on Snapshot DAOs.”, https://arxiv.org/abs/2211.15993, 2022.
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Abstract:

The solutions to quadratic equations are invaluable in real-world problems due to their widespread applications, such as profit determination of products, calculation of areas, and speed formulation of an object. The classical techniques for solving quadratic equations with closed-form solutions are factorization, completing the square, the quadratic formula, and graphical methods. This study introduces a novel, informative, and computationally innovative technique known as the symmetric average approach (SAA) for solving quadratic equations. The symmetric average approach (SAA) involves the identification of the mid-value given the solutions of the equation and the homologous deviation. Contrary to the classical methods, particularly the formula method that requires direct coefficient substitutions resulting in algebraic complexity, which typically burdens learners, the symmetric average approach (SAA) centers on the symmetry of the roots of the equation. Numerical validation reveals that the technique improves theoretical clarity and is capable of enhancing learners’ ability to retain quadratic relations. The technique is a valid empirical alternative and pedagogical tool for solving quadratic equations.

Keywords:

Algebra,Formula Method,Quadratic Equation,Symmetric Average Approach.,

References:

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Abstract:

Wireless Sensor Networks have become increasingly significant in the 21st century, enabling a wide spectrum of critical applications. However, energy efficiency remains a fundamental limitation affecting their scalability, reliability, and sustainability. This study introduces a novel framework that integrates Intelligent Dolphin Echolocation Optimization (IDEO) with Fuzzy C-Means clustering for adaptive Cluster Head (CH) selection. The proposed method employs a multi-criteria strategy considering residual energy, transmission distance to the Base Station (BS), and latency minimization, thereby ensuring efficient data aggregation in dynamic environments. Furthermore, an Energy-Conscious Cognitive Smart Routing Protocol (EC2-SRP) is developed to establish both energy-efficient and shortest routing paths, reducing communication overhead and extending network longevity. The novelty of this work lies in (i) the hybrid integration of IDEO with FCM for adaptive clustering, (ii) multi-criteria CH selection for enhanced energy efficiency, (iii) the design of a cognitive routing protocol balancing energy awareness with path optimality, and (iv) a comprehensive QoS-based evaluation. Simulation results demonstrate superiority over existing methods in terms of energy consumption, network lifetime, packet delivery ratio (PDR), End-to-End Delay, routing overhead, and Fault Tolerance, thereby achieving higher energy efficiency, reliability, and sustainability in Wireless Sensor Networks.

Keywords:

Clustering; Cross-layer routing; Fuzzy-C Means; Optimization; Wireless Sensor Network; Optimization,

References:

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