Journal Vol – 21 No – 3, March 2026

Abstract:

This article suggests enhancing the hydraulic pipe-bending machine by transitioning from manual to semi-automatic and fully automatic modes, tailored for manufacturing truck front bull bars. The control system integrates a PLC, an HMI, sensors, and a CAD file processing application, enabling input of X-Y coordinates or control derived from CAD drawings to achieve precise bending paths and high consistency. The findings indicate improved machining efficiency and a notable reduction in bending errors.

Keywords:

Semi-automatic and automatic operation,Truck front bull bar,PLC,HMI,Sensors,CAD,

References:

I. Ejiko, S.O., 2Maliki O.B., 3Olakolegan O. D, “Development of sheet metal bending machine”, Global Scientific Journal, Volume 12, Issue 9, September 2024, pp. 1467-1481
II. Guo, Dongxu, Qun Sun, Ying Zhao, Shangsheng Jiang, and Yigang Jing, “System Design and Experimental Study of a Four-Roll Bending Machine”, Applied Sciences 15, no. 13 (June 30, 2025): 7383. 10.3390/app15137383
III. Jiang, Ying, Xiaofei Cheng, Hongji Li, Xinghua Ren, and YueYang Li, “Structural Design of Steel Bar Bending Machine”, Journal of Physics: Conference Series 1986, no. 1 (August 1, 2021): 012097. 10.1088/1742-6596/1986/1/012097
IV. Mahesh Laxman Rathod, Avadhut Balkrishna Kadam, Balkrushna Popat Kale, Vijay Gorakhnath Kamble, Rohan Gulab Mali, “Design and Development of Pipe Bending Machine”, International Journal of Manufacturing and Production Engineering, 2025, 03 (01), pp.22-30
V. Jenan Mohammed Naje, “Effect of Radius and Angle of Bending on the Concentration of Stresses in the Aluminum Sheet”, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES 14, no. 5 (October 28, 2019). 10.26782/jmcms.2019.10.00028
VI. Pham Bui Quang Khai, Nguyen Anh Duc, Ngo Tien Thanh, Le Linh, "Design and manufacture of automatically shaped steel bending machine", Graduation project, Ho Chi Minh City University of Technology and Education (HCMUTE), 2023. https://thuvienso.hcmute.edu.vn/doc/thiet-ke-va-che-tao-may-uon-thep-dinh-hinh-tu-dong-864549.html

VII. Pham Thi Thu Hien, Nguyen Quoc Loc, Vo Hong Nhut, Design and manufacture of tube and V steel bending machine, Mekong University Scientific Journal, 2022, No. 6, pp. 49-55. https://vjol.info.vn/index.php/dhcl/article/view/68200
VIII. Shantanu Garad, “Design and Development of Automatic Bending Machine”, International Journal of Engineering Research & Technology, no. 06 (July 4, 2020). 10.17577/ijertv9is060977
IX. Vipul, Navale, S M Kiran, S. Rahul, Takdunde Kanchan, and Tekale Valhaji. “Design and Fabrication of Hydraulic Rod Bending Machine.” Journal of emerging technologies and innovative research (2019)

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Abstract:

In this paper, we establish the existence of denumerably many positive solutions for the iterative system of nonlinear four-point tempered fractional-order boundary value problems. By an application of Krasnoselskii’s fixed point theorem in a Banach space. An illustrative example is also presented.

Keywords:

Tempered FDE,,iterative,fixed point theorem,

References:

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II. Ahmadini, A.A.H., Khuddush, M., & Rao, S. N. (2023). Multiple positive solutions for a system of fractional order BVP with p-Laplacian operators and parameters, Axioms, 12 (934). 10.3390/axioms12100974
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XII. Hamou, A. A., Hammouch, Z., & Azroul, E., & Agarwal, P. (2022). Monotone iterative technique for solving finite difference systems of time fractional parabolic equations with initial/ periodic conditions, Appl. Numer. Math., 181, 561–593. 10.1016/j.apnum.2022.04.022
XIII. Johansyah, M. D., Supriatna, A. K., Rusyaman, E., & Saputra, J. (2021). Application of fractional differential equation in economic growth model: A systematic review approach, AIMS Math., 9 (6), 10266-10280. 10266-10280. 10.3934/math.2021594
XIV. Kilbas, A. A., Srivastava, H. M., & Trujillo, J. J. (2006). Theory and applications of fractional differential equations, Elsevier.
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XVI. Khuddush, M. (2023). Existence of solutions to the iterative system of nonlinear two-point tempered fractional order boundary value problems, Adv. Stud.-Euro-Tbil. Math., 16 (2), 97-144. 10.32513/asetmj/193220082319
XVII. Khuddush, M., Prasad, K. R., & Leela, D. (2022). Existence of solutions for an infinite system of tempered fractional order boundary value problems in the spaces of tempered sequences, Turkish J. Math., 46(2), 433-452. Article 8. 10.3906/mat-2106-110
XVIII. Khuddush, M., Prasad, K. R., & Leela, D. (2020). Existence of solutions for infinite system of regular fractional Sturm-Liouville problems in the spaces of tempered sequences, Tbilisi Mathematical Journal, 13 (4), 193–209. 10.32513/tbilisi/1608606058
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Abstract:

This research introduces an innovative text encryption method based on fuzzy logic, featuring per-session key generation using Ks = HMAC- SHA256(Km, N || session_id), non-deterministic character substitution, and six rounds of Substitution-Permutation Network processing on 8-character blocks. The design eliminates positional leakage through cross-block diffusion and secret-dependent transformations while providing provable IND-CPA security with adversary advantage bounded by 2???. Extensive testing confirms 49.7% ± 1.2% avalanche effect, conditional entropy H(C|P) = 3.91 bits/character (91% of theoretical 4.32 maximum), and 17.3?s encryption time for 8-byte blocks on standard computing platforms.

Keywords:

Fuzzy Logic,SPN Cryptography,IND-CPA Security,Avalanche Effect,Conditional Entropy,

References:

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VIII. Jin, Jie, et al. "A fuzzy activation function based zeroing neural network for dynamic Arnold map image cryptography." Mathematics and Computers in Simulation 230 (2025): 456-469. 10.1016/j.matcom.2024.10.031
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Abstract:

Timely intervention of gastrointestinal (GI) diseases, namely polyps, ulcerative colitis, and esophagitis are critical for improving the quality of human life and reducing the mortality rate associated with these conditions. Hence, early detection and diagnosis of GI disease are essential because they can reduce the severity of the disease. Traditional medical imaging techniques are time-intensive, labor-intensive, and susceptible to human error. Recently, deep learning models have been extensively used for image classification tasks, and they are consistently achieving promising results in real-time decision-making. However, the conventional deep learning models struggle with overfitting and poor generalization on medical imaging datasets because of the wide variability in disease types. To address this issue, a Hybrid Attention Convolutional Neural Network (HA-CNN) is proposed in this analysis. This proposed model integrates the strength of the convolutional operation and attention mechanism to focus on discriminative regions and features in medical images. The hybrid model is designed for high variability and complex features in the medical images. This model can accurately recognise lesion regions and detect types of diseases, and avoids overfitting. The effectiveness of the proposed HA-CNN is evaluated using a benchmark dataset, namely the Kvasir dataset, using 5-fold stratified cross-validation. The model achieves a mean classification accuracy of 94.43% ± 0.58, outperforming existing comparative methods. Moreover, the integration of empirical mode decomposition and dynamic scaling enhanced the quality of training data by improving the generalization ability of the model. By overcoming the existing challenges, this framework focuses on improving the diagnostic process in medical imaging, resulting in the precise detection of GI diseases.

Keywords:

Gastrointestinal Disease,Stomach Ulcers,Deep Learning,Convolutional Neural Network,Empirical Mode Decomposition,Endoscopy Imaging.,

References:

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Abstract:

Let R be a 2-torsion-free semiprime ring, F:R→R be a generalized Jordan (σ,τ)-derivation associated with Jordan (σ,τ)-derivation d, and H:R→R be a left σ-centralizer. If (i) F(x^2 )∓H(x^2 )=0 (ii) F(x^2 )∓H(x^2 )∈C_(σ,τ); for all x,y∈R.

Keywords:

Semiprime ring,Derivation,Jordan derivation,Generalized derivation,(σ, τ) -derivation,Generalized (σ, τ) -derivation,Jordan (σ, τ) -derivation,Generalized Jordan (σ, τ) - derivation.,

References:

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Abstract:

This paper proposes a new technique for the recognition of emotion from whispered speech, integrating advanced techniques in the extraction of features, feature selection, and classification to enhance accuracy and robustness. The approach begins with extracting three types of features: wavelet features for multi-resolution analysis, prosodic features for pitch and intensity, and spectral features such as formants, Mel-Frequency Cepstral Coefficients (MFCCs), and Long-Term Average Spectrum (LTAS) to capture comprehensive emotional information. A two-step feature selection process, involving partial correlation analysis and Linear Discriminant Analysis (LDA), is deployed to identify and retain the most informative features while reducing dimensionality. Classification is performed using an ensemble learning strategy that associates Support Vector Machine (SVM) and Decision Tree classifiers, with SVM distinguishing between neutral and emotional states, and the Decision Tree further categorizing emotions. Simulation results using the GeWEC dataset show that the suggested approach is effective, achieving significant improvements in Unweighted Average Recall (UAR) across various configurations. This underscores the method’s ability to exactly identify emotional states from whispered speech, offering valuable insights for real-world applications in emotion recognition systems.

Keywords:

Emotion Recognition,Whispered Speech,Wavelet Features,Prosodic Features,Spectral Features MFCCs,LDA,Ensemble Learning,

References:

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XXXVI. Zhang, Li, and Ying Zhao. "Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering." IEEE Transactions on Audio, Speech, and Language Processing, vol. 31, no. 7, 2023, pp. 1234-1245.
XXXVII. Zhaofeng Lin, Tanvina Patel, Odette Scharenborg, “Improving Whispered Speech Recognition Performance Using Pseudo-Whispered Based Data Augmentation”, 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) – Taipei, Taiwan.

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Abstract:

This article introduces a new semicircular distribution known as the stereographic semicircular new Weibull-Pareto distribution. We explore its various properties and provide explicit expressions for trigonometric moments. We delve into its essential mathematical properties and execute a simulation study to estimate its parameter values. Furthermore, we showcase the suggested distribution's modeling capacity in the context of real-world phenomena. An illustrative example is conducted using an authentic data set within the geological domain.

Keywords:

Axial data,semicircular model,inverse stereographic projection,trigonometric moments,simulation.,

References:

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XVI. S.R.Jammalamadaka and A.Sengupta(2001). Topics in Circular Statistics, World Scientific Publishing, Singapore. https://doi.org/10.1142/4031.
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XVIII. S.Joshi, S. and K.K.Jose.(2018). Wrapped Lindley distribution. Communications in Statistics-Theory and Methods.47(5),1013-1021. 10.1080/03610926.2017.1280168.

XIX. Salah, H.A., H. Al-N, Nadia and A.E. Najm.(2023). Two doubly truncated semicircular distribution:Some important properties, Mustansiriyah Journal of Pure and Applied Sciences. 1 (1),164–174. 10.47831/mjpas.v1i1.18.
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XXII. P. Yedlapalli, S.V.S. Girija, A.V.D. Rao, and Sastry, K.L.N.( 2020).A new family of semicircular and Circular Arc tan-exponential type distribution.Thai Journal of Mathematics. 18(2),775-781.
XXIII. P. Rafael D. Marinho, Rodrigo B. Silva, Marcelo Bourguignon, Gauss M. Cordeiro, and N.Saralees. Adequacy Model: An R package for probability distributions and general purpose optimization. PLoS ONE, 14(8),(2019),1-30. 10.1371/journal.pone.0221487.
XXVI. Rao, A.V.D., I.R.Sharma, and S.V.S.Girija.(2007) .On Wrapped version of some life testing models. Communications in Statistics: Theory and Methods. 36 (11),2027-2035-1021. https://doi.org/10.1080/03610920601143832.
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XXVII. Suleman N., Albert L.(2015).The new Weibull-Pareto distribution.Pak. Stat. oper.res. 11(1),103-114. 10.18187/pjsor.v11i1.863.
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Abstract:

The integration of Sparse Spatial Graph Neural Network (SSGNN) is a promising approach for enhancing the security and performance of Multiple User - Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing (MU-MIMO-OFDM) systems. SSGNN can effectively model the sparse channel structure and estimate the channel state information (CSI) in real-time. This research introduces AI-driven solutions for next-generation wireless systems, focusing on a Sparse Spatial Graph Neural Network (SSGNN) optimized with Fennec Fox Optimization (FFO) for secure multi-user MIMO-OFDM channel estimation and interference mitigation. The proposed SSNGN-FFO approach achieves exceptional performance, with a remarkably low Bit Error Rate (BER) of 0.00012 and a high Peak Signal-to-Noise Ratio (PSNR) of 45dB, indicating its potential for reliable and high-quality wireless communication using MATLAB.

Keywords:

Bit Error Rate,Channel Estimation,Channel State Information,Deep Learning,Inter-Carrier Interference,Mean Squared Error,MU-MIMO-OFDM,Peak Signal-to-Noise Ratio,Sparse Spatial Graph Neural Network,

References:

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II. Catak, F. O., et al. “Defensive Distillation-Based Adversarial Attack Mitigation Method for Channel Estimation Using Deep Learning Models in Next-Generation Wireless Networks.” IEEE Access, vol. 10, 2022, pp. 98191–98203. 10.1109/ACCESS.2022.3206385.
III. Chitikena, R., and P. E. Rani. “Deep Learning-Based Channel Estimation and Secure Data Transmission Using IEHO-DLNN and MECC Algorithm in MU-MIMO OFDM System.” Wireless Personal Communications, vol. 129, no. 4, 2023, pp. 2269–2289. 10.1007/s11277-023-10172-2.
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VI. Farzamnia, A., N. W. Hlaing, M. K. Haldar, and J. Rahebi. “Channel Estimation for Sparse Channel OFDM Systems Using Least Square and Minimum Mean Square Error Techniques.” Proceedings of the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1–5. IEEE. 10.1109/ICEngTechnol.2017.8308193.
VII. Gao, J., C. Zhong, G. Y. Li, and Z. Zhang. “Deep Learning-Based Channel Estimation for Massive MIMO with Hybrid Transceivers.” IEEE Transactions on Wireless Communications, vol. 21, no. 7, 2022, pp. 5162–5174. 10.1109/TWC.2021.3137354.
VIII. Giannopoulos, T., and V. A. Paliouras. “A Low-Complexity PTS-Based PAPR Reduction Technique for OFDM Signals without Transmission of Side Information.” Journal of Signal Processing Systems, vol. 56, 2009, pp. 141–153. 10.1007/s11265-008-0238-y.
IX. Jiang, W., et al. “Massive Connectivity over MIMO-OFDM: Joint Activity Detection and Channel Estimation with Frequency Selectivity Compensation.” IEEE Transactions on Wireless Communications, vol. 21, no. 9, 2022, pp. 6920–6934. 10.1109/TWC.2022.3153106.
X. Kapileswar, N., and P. P. Kumar. “Optimized Deep Learning Driven Signal Detection and Adaptive Channel Estimation in Underwater Acoustic IoT Networks.” International Journal of Communication Systems, vol. 37, no. 4, 2024, e5673. 10.1002/dac.5673.
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XIII. Kundu, N. K., et al. “Channel Estimation and Secret Key Rate Analysis of MIMO Terahertz Quantum Key Distribution.” IEEE Transactions on Communications, vol. 70, no. 5, 2022, pp. 3350–3363. 10.1109/TCOMM.2022.3161008.
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XVII. Mayouche, A., W. A. Martins, S. Chatzinotas, and B. Ottersten. “Data-Driven Precoded MIMO Detection Robust to Channel Estimation Errors.” IEEE Open Journal of the Communications Society, vol. 2, 2021, pp. 1144–1157. 10.1109/OJCOMS.2021.3079643.
XVIII. Muni, N. B., et al. “Integrating Edge Computing with Swarm Intelligence for Efficient IoT Device Management.” Proceedings of the 3rd International Conference on Data Science and Information System (ICDSIS), Hassan, India, 2025, pp. 1–5. IEEE. 10.1109/ICDSIS65355.2025.11070424.
XIX. Nandi, S., A. Nandi, and N. N. Pathak. “Channel Estimation of Massive MIMO-OFDM System Using Elman Recurrent Neural Network.” Arabian Journal for Science and Engineering, vol. 47, 2022, pp. 9755–9765. 10.1007/s13369-021-06366-0.
XX. Nandi, S., A. Nandi, and N. N. Pathak. “Deep Learning Assisted Technology for MIMO-OFDM 5G Application.” Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies, 2022. 10.1007/978-981-16-6893-7.
XXI. Nandi, S., A. Nandi, and N. N. Pathak. “Spectrally Efficient MIMO-OFDM for THz Communication in FANETs.” Proceedings of the 14th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1–7. 10.1109/ICCCNT56998.2023.10306931
XXII. Nandi, S., et al. “Performance Analysis of Cyclic Prefix OFDM Using Adaptive Modulation Techniques.” International Journal of Electrical, Electronics and Computer Systems, vol. 6, no. 8, 2017, pp. 214-220. https://zenodo.org/record/5274429/files/J1_2017_IJEECS.pdf.
XXIII. Nandi, S., N. N. Pathak, and A. Nandi. “A Novel Adaptive Optimized Fast Blind Channel Estimation for Cyclic Prefix Assisted Space–Time Block Coded MIMO-OFDM Systems.” Wireless Personal Communications, vol. 115, 2020, pp. 1317–1333. 10.1007/s11277-020-07629-z.
XXIV. Nandi, S., N. N. Pathak, and A. Nandi. “Analysis of Hard Decision and Soft Decision Decoding Mechanism Using Viterbi Decoder in Presence of Different Adaptive Modulations.” International Journal of Future Generation Communication and Networking, vol. 13, no. 3, 2020, pp. 3002–3012. 10.5281/zenodo.5273662.
XXV. Nandi, S., N. N. Pathak, and A. Nandi. “Avenues to Improve Channel Estimation Using Optimized CP in STBC Coded MIMO-OFDM System—A Global Optimization Approach.” Proceedings of the 5th International Conference on Microelectronics, Computing and Communication Systems (MCCS), Ranchi, India, 2020. 10.1007/978-981-16-0275-7_21.
XXVI. Nandi, S., N. N. Pathak, and A. Nandi. “Efficacy of Channel Estimation and Efficient Use of Spectrum Using Optimised Cyclic Prefix (CP) in MIMO-OFDM.” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, 2019, pp. 3032–3038. 10.35940/IJEAT.B4093.129219.
XXVII. Nandi, S., N. N. Pathak, and A. Nandi. “Implementation of Adaptive Optimized Fast Blind Channel Estimation of MIMO-OFDM Systems Using MFPA.” Intelligent Multi-Modal Data Processing, Wiley, 2020, pp. 165–188. 10.1002/9781119571452.ch8.
XXVIII. Sarkar, M., and S. Ghosh. “Development of a Secured Optical Code Division Multiple Access System by Implementing Hybrid 2D Modified Walsh Code.” Optical Engineering, vol. 59, no. 10, 2020, p. 106107. 10.1117/1.OE.59.10.106107.
XXIX. Sarkar, M., and S. Ghosh. “Implementation of Designed OCDMA Code in RoF for Future 5G Communication.” Journal of Optics, 2024. 10.1007/s12596-024-01813-1.
XXX. Shankar, R., S. Nandi, and A. Rupani. “Channel Capacity Analysis of Non-Orthogonal Multiple Access and Massive Multiple-Input Multiple-Output Wireless Communication Networks Considering Perfect and Imperfect Channel State Information.” Journal of Defense Modeling and Simulation, vol. 19, no. 4, 2021, pp. 771–781. 10.1177/15485129211000139.
XXXI. Sivadas, N. A. “PAPR Reduction of OFDM Systems Using H-SLM Method with a Multiplier-Less IFFT/FFT Technique.” ETRI Journal, vol. 44, 2022, pp. 379–388. 10.4218/etrij.2020-0316.
XXXII. Soni, C., and N. Gupta. “An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System.” International Journal of Electronics, 2024, pp. 1–23. 10.1080/00207217.2024.2408797.
XXXIII. Tang, R., C. Qi, and P. Zhang. “Block Sparse Channel Estimation Based on Residual Difference and Deep Learning for Wideband MmWave Massive MIMO.” Proceedings of the IEEE 97th Vehicular Technology Conference (VTC-Spring), Florence, Italy, 2023, pp. 1–6. 10.1109/VTC2023-Spring57618.2023.10200898.
XXXIV. Trojovská, E., M. Dehghani, and P. Trojovský. “Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm.” IEEE Access, vol. 10, 2022, pp. 84417–84443. 10.1109/ACCESS.2022.3197745.
XXXV. Venkateswarlu, C., and N. V. Rao. “An Efficient MAPSO Model for Interference Cancellation and Optimal Channel Estimation in MIMO-OFDM System.” Wireless Personal Communications, vol. 128, no. 1, 2023, pp. 283–307. 10.1007/s11277-022-09955-w.
XXXVI. Wu, H., X. Li, and Y. Deng. “Deep Learning-Driven Wireless Communication for Edge-Cloud Computing: Opportunities and Challenges.” Journal of Cloud Computing, vol. 9, no. 1, 2020, p. 21. 10.1186/s13677-020-00168-9.
XXXVII. Yuan, Y., et al. “Alpine Skiing Optimization: A New Bio-Inspired Optimization Algorithm.” Advances in Engineering Software, vol. 170, 2022, p. 103158. 10.1016/j.advengsoft.2022.103158.
XXXVIII. Zhang, M., and P. Li. “Nested Graph Neural Networks.” Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 15734-15747. 10.48550/arXiv.2110.13197.
XXXIX. Zheng, K., and X. Ma. “Designing Learning-Based Adversarial Attacks to MIMO-OFDM Systems with Adaptive Modulation.” IEEE Transactions on Wireless Communications, 2023.
https://www.scribd.com/document/744142171/Designing-Learning-Based-Adversarial-Attacks-to-MIMO-OfDM-Systems-With-Adaptive-Modulation.

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Abstract:

For wireless applications, a compact microstrip monopole is designed, produced, and measured based on C slots and etched Stepped Impedance Resonators (SIRs). This antenna functions with two C slots with SIR slot at 3.1 and 3.9 GHz bands, respectively, and displays bi-direction and omnidirectional radiation patterns in the E and H-planes. The WiMAX band spans from 2.63 to 3.16 GHz, while the C-band spans from 3.32-4.21 GHz. The antenna's size is a mere 30 × 20 × 1.6 mm3. With a dielectric constant of ?r = 4.4, a loss tangent (tan ?)= 0.015, and a thickness of 1.6 mm, the antenna on a FR4 substrate exhibits an impedance bandwidth ranging from 2.23 GHz to 6.9 GHz, according to the observed data. In the end, the simulations are validated by fabricating and measuring the antenna that was projected.

Keywords:

Monopole,Microstrip,WiMAX,WLAN,UWB antenna,Decagon patch,dual band-notched,

References:

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Abstract:

The role of management is to evaluate and validate the objectives of an organization. The management’s role in state-owned business enterprises is more critical due to the influence of the existing human resource performance management system. The organization’s intelligence helps to gather important data from the large unstructured data and modifies it into useful data to improve the efficiency and productivity of the organization. In the era of the Internet, conventional performance management struggled to meet the modern development of an enterprise. Hence, organizations must continuously innovate and improve their performance management strategies. Deep learning has shown potential in enhancing business intelligence with the automated validation of large and complex data sources. Nevertheless, it has not achieved much attention as they are not efficient in decision making process within the organization. Therefore, in this article, an advanced deep learning-based network is designed for effective decision-making to enhance the growth of a business organization. Initially, the necessary data for the analysis is taken from the available resources. Subsequently, the significant features from the data are extracted using the T-distributed Stochastic Neighbor Embedding (T-SNE), Principal Component Analysis (PCA) and statistical features. The extracted features are combined using the Cross Attention-based Feature Fusion (CAFF). In the end, the resultant fused features are given to Dense Bidirectional Long Short-Term Memory (D-BiLSTM) for performing efficient decision-making. Finally, comparative analysis is conducted to validate the functionality of the model. The result demonstrates that the designed framework is more efficient in decision-making to enhance the productivity of business organizations.

Keywords:

Performance Management,Business Organization,T-distributed Stochastic Neighbor Embedding,Cross Attention-based Feature Fusion,Dense Bidirectional Long Short-Term Memory,

References:

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Abstract:

This paper presents a unified mathematical framework for modeling the pharmacokinetics of the drug delivery through both oral (gastrointestinal) and intravenous pathways of Khanday et. al. [XIV] The structure is formulated using the Caputo–Fabrizio fractional derivative with a non-singular exponential kernel, offering a more realistic description of the memory and diffusion processes compared to classical integer-order and singular fractional operators. Theoretical analysis is conducted to ensure the existence and uniqueness of a solution, applying the fixed-point theorem as the core analytical tool. Laplace transform techniques are employed to obtain explicit solutions, and the dynamic behaviour of the drug concentration in the bloodstream is illustrated through numerical simulations. The results highlight the influence of the fractional-order parameters on drug absorption and distribution, offering valuable insights for biomedical and pharmaceutical applications.

Keywords:

,

References:

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