Research Publications

@article{702,

title = {Design and implementation of discontinuities in Microwave filter design},

author = {C Lakshmi and K. P and D. Kavitha and D. Kannadassan},

url = {https://www.tandfonline.com/doi/full/10.1080/1448837X.2025.2583477},

doi = {10.1080/1448837X.2025.2583477},

isbn = {1448837X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Australian Journal of Electrical and Electronics Engineering},

publisher = {Taylor and Francis Ltd.},

abstract = {The systematic design and implementation of microwave circuits play a crucial role in the microwave industry. The traditional method of design is based on trial-and-error methods, leading to approximation-based structures. This research work primarily focused on the analysis, design, and implementation of discontinuities in slotted resonator structures, achieving empirical modelling of the coupled resonator structure for the implementation of bandstop filter design. This work leads to a generic systematic design of a microwave structure, like a filter, based on the developer specification. The systematic design and implementation of a band-stop filter using coupled resonator theory for a fractional bandwidth of less than 20% is discussed. Also, a study on the modelling of discontinuity-based structures with elliptical modelling techniques is performed.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{701,

title = {Characterization of Polypyrrole Based Novel High Dielectric Materials for Low Frequency Applications},

author = {L. B. Gunjal and S. Manjunatha and C. H. V. V. Ramana and P. N. R. Shankar and H. L. Pushpalatha and Y. T. Ravikiran and N. M. Nagabhushana},

url = {https://link.springer.com/article/10.1007/s42341-025-00686-w},

doi = {10.1007/s42341-025-00686-w},

isbn = {12297607 (ISSN); 20927592 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Transactions on Electrical and Electronic Materials},

publisher = {Korean Institute of Electrical and Electronic Material Engineers},

abstract = {Neodymium oxide (Nd<inf>2</inf>O<inf>3</inf>) embedded in polypyrrole (PPy) with varying compositions were synthesized using the chemical polymerization technique. PPy and PPy/Nd<inf>2</inf>O<inf>3</inf> composites were employed to the several characterization techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and selected area electron diffraction (SAED), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) studies. Room temperature AC conductivity studies were carried out in the frequency range 50 Hz to 5 MHz. PPy/Nd<inf>2</inf>O<inf>3</inf>-10 wt% composite exhibited highest conductivity of 2.69 × 10−² Scm− 1 among all the composites and an increase of two orders compared to PPy, which is noteworthy. Cole-Cole plots for the composites exhibited a non-Debye type relaxation. Interestingly, dielectric constant of PPy/Nd<inf>2</inf>O<inf>3</inf>-10 wt% composite in the frequency range 1–100 kHz was found to be high ≈ 1000. Also, the low frequency relaxation peaks, low dielectric loss of the composites makes them potential candidates in the design of low frequency operating devices.},

note = {0},

keywords = {PHY},

pubstate = {published},

tppubtype = {article}

}

@article{697,

title = {Experimental exergy analysis of SnO2 nanofluid photovoltaic thermal system using machine learning approach},

author = {B. Poorani and R. C. Sreevidya and R. Vijayakumar and M. Varalatchoumy and P. Natesan and P. Prabhakar},

url = {https://link.springer.com/article/10.1007/s10973-025-14938-7},

doi = {10.1007/s10973-025-14938-7},

isbn = {13886150 (ISSN); 15882926 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Thermal Analysis and Calorimetry},

publisher = {Springer Science and Business Media B.V.},

abstract = {The efficiency of photovoltaic thermal (PVT) systems is often hindered by high operating temperatures, which can be effectively addressed through advanced cooling methods. This study explored the use of a water-based tin dioxide (SnO<inf>2</inf>) nanofluid at a 0.1% concentration as an enhanced coolant to boost the system’s exergy efficiency. The research involved experimental testing under three distinct flow rates—0.5, 1.0 and 1.5 LPM—to evaluate the nanofluid’s performance. The results confirmed that the nanofluid offered a significant advantage over conventional pure water cooling. Specifically, at the highest flow rate of 1.5 LPM, the maximum exergy efficiency improved remarkably from 11.1 to 18.9%. In addition to the experimental work, the study also developed and tested several machine learning (ML) models to predict the system’s performance. Two primary models, K-Nearest Neighbor (KNN) and Support Vector Regression (SVR), were utilized. The researchers also investigated the impact of integrating Wavelet Transform (WT), a signal-processing technique, with these ML models. The results demonstrated that the SVR model combined with Wavelet Transform (SVR-WT) provided the most accurate predictions on the test dataset. This model achieved an impressive coefficient of determination (R2) of 0.885, indicating a strong correlation between the predicted and actual values. Its predictive capability was further highlighted by a low root mean square error (RMSE) of 2.196 and a mean absolute error (MAE) of 3.086. Overall, the findings conclusively establish that SnO2 nanofluid is an excellent coolant for enhancing PVT system performance, and that the SVR-WT model offers a reliable predictive framework for optimizing these systems.},

note = {0},

keywords = {AIML},

pubstate = {published},

tppubtype = {article}

}

@article{693,

title = {Performance enhancement of photovoltaic thermal collectors using water based MnO2 nanofluids and machine learning models},

author = {M. P. Rajakumar and Senthil Kumar and B. Srimanickam and S. Srividhya and K. Elangovan and N. Kaliappan and Kamakshi Priya},

url = {https://www.nature.com/articles/s41598-025-23505-x},

doi = {10.1038/s41598-025-23505-x},

isbn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This study investigates the enhancement of Photovoltaic-Thermal (PVT) collector performance through the combined use of water-based manganese dioxide (MnO<inf>2</inf>) nanofluids and machine learning (ML) models. Conventional PVT systems often suffer from elevated operating temperatures that degrade photovoltaic efficiency. To address this challenge, the research employs MnO<inf>2</inf> nanoparticles—known for their stability, cost-effectiveness, and high thermal conductivity—dispersed in water to improve thermal regulation within the PVT system. Experimental evaluations were conducted at three flow rates (0.5, 1.0, and 1.5 LPM) to assess thermal and electrical performance. The MnO<inf>2</inf> nanofluid-based PVT collector demonstrated superior power output (ranging from 80.42 W to 202.91 W) compared to water-cooled PVT (72.48 W to 176.17 W) and standalone PV systems (64.23 W to 152.36 W). A peak electrical efficiency of 14.58% was observed at 0.5 LPM, while glazing surface temperatures during midday ranged between 52.03 °C and 54.60 °C, indicating effective thermal management. To predict system behavior and performance, machine learning models—including Random Forest (RF), Radial Basis Function (RBF), and Multilayer Perceptron (MLP)—were applied. Among these, the RBF model achieved the highest predictive accuracy, with R2 values of 0.96 for power output and 0.97 for electrical efficiency on the testing dataset. Overall, this integrated experimental-ML approach not only confirms the thermal and electrical advantages of MnO<inf>2</inf> nanofluids but also demonstrates the potential for intelligent optimization and control in high-performance solar energy systems.},

note = {0},

keywords = {MECH},

pubstate = {published},

tppubtype = {article}

}

@article{692,

title = {Impact of additive manufacturing techniques on metal based composites: a brief review},

author = {V. Veerasimman and K. Prasath and S. Rajendran and Y. -L. Yang and S. Sivanandam},

url = {https://link.springer.com/article/10.1007/s43939-025-00432-2},

doi = {10.1007/s43939-025-00432-2},

isbn = {27307727 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Discover Materials},

volume = {5},

publisher = {Discover},

abstract = {This review examines the fundamental principles, processes, and methods of metal-based 3D printing, also known as additive manufacturing (AM), which constructs parts layer by layer directly from digital models. Metal AM enables the fabrication of fully dense metallic components with high precision and speed, making it highly suitable for producing complex, high-performance parts across mechanical, civil, and medical industries. Among various AM technologies, powder bed fusion (PBF) is a leading method, which selectively fuses powder layers using an energy source such as a laser. PBF offers notable advantages in terms of material stability, mechanical durability, and cost-effective production. Unlike existing reviews that typically focus on either specific process techniques or application areas, this manuscript provides a comprehensive and holistic perspective by integrating process mechanisms, material considerations, post-processing effects, and industrial applications. By critically analyzing previous research, this review addresses current limitations, such as parameter optimization and mechanical property enhancement, and highlights emerging opportunities, including the development of efficient, low-cost production strategies. This review thereby provides a clear direction for future research and advancements in metal additive manufacturing technologies.},

note = {0},

keywords = {MECH},

pubstate = {published},

tppubtype = {article}

}

@article{691,

title = {Electromagnetic Analysis and Machine Learning-Driven Optimization of a Graphene-Based Metasurface Sensor for Waterborne Pathogen Detection},

author = {V. Kalaipoonguzhali and R. Vijayakumar and A. Madhukumar and U. Arunkumar},

url = {https://link.springer.com/article/10.1007/s11220-025-00675-6},

doi = {10.1007/s11220-025-00675-6},

isbn = {15572064 (ISSN); 15572072 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Sensing and Imaging},

volume = {26},

publisher = {Springer},

abstract = {Waterborne bacterial contamination represents a critical global health challenge, with traditional detection methods suffering from lengthy processing times, complex sample preparation, and limited real-time monitoring capabilities. This research develops a metasurface biosensor incorporating graphene-enhanced multilayer resonators for rapid, label-free detection of waterborne bacteria such as Escherichia coli, Vibrio cholerae, and Salmonella species. Electromagnetic analysis employing Maxwell’s equations, Kubo formalism for graphene conductivity, and coupled-mode theory provides comprehensive theoretical foundation for the sensor’s operation. Using COMSOL Multiphysics for numerical simulations, the sensor achieves a maximum sensitivity of 487.805 GHz/RIU and a figure of merit of 19.5122 RIU⁻¹ over the operating frequency range of 0.168–0.176 THz, demonstrating remarkable performance characteristics. As the sensitivity to changes in the refractive index increases, the detection limit drops substantially from 0.548421 RIU to 0.064244 RIU. Machine learning integration using polynomial regression algorithms achieves R² scores of 86% and 89% for predicting absorption characteristics based on graphene chemical potential and incident angle parameters. The proposed design establishes a scalable pathway toward real-time terahertz biosensing for next-generation water quality monitoring and microbial diagnostics.},

note = {0},

keywords = {CIVIL},

pubstate = {published},

tppubtype = {article}

}

@article{690,

title = {Performance evaluation and machine learning-based prediction of PCM-integrated solar chimney drying for black dates},

author = {R. N and B. Srimanickam and E. K and S. P and K. Mehar and R. P. Singh and K. P. K},

url = {https://www.sciencedirect.com/science/article/pii/S2590123025042641?via%3Dihub},

doi = {10.1016/j.rineng.2025.108218},

isbn = {25901230 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Results in Engineering},

volume = {28},

publisher = {Elsevier B.V.},

abstract = {This study presents a comparative evaluation of black date drying using three solar-based methods: open sun drying, a solar chimney dryer, and a solar chimney dryer integrated with phase change material (PCM). In parallel, machine learning (ML) models were employed to predict and optimize system performance. Experimental findings reveal that the PCM-integrated solar chimney significantly outperformed conventional approaches, achieving peak thermal and drying efficiencies of 49 % and 59 %, respectively, compared to 20 % for open sun drying and 41 % for the standalone solar chimney. The latent heat storage of PCM extended effective drying into late hours, sustaining 25 % efficiency at 16:00 h against only 11 % under open sun drying. Among the tested ML models—multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR)—the MLP demonstrated the highest predictive accuracy (training: RMSE = 0.85, R² = 0.92; testing: RMSE = 1.10, R² = 0.90). Feature importance analysis further identified solar irradiance and airflow as dominant parameters governing drying performance. By integrating PCM-based thermal management with AI-driven prediction, this work establishes a scalable, energy-efficient drying solution to mitigate agricultural post-harvest losses, directly supporting global initiatives on sustainable food processing and renewable energy utilization.},

note = {0},

keywords = {MECH},

pubstate = {published},

tppubtype = {article}

}

@article{689,

title = {Experimental and machine learning-based analysis of peanut drying using solar Photovoltaic-Thermal (PVT) collectors with forced convection and latent heat storage},

author = {N. V. Van Minh and B. Poorani and R. Vijayakumar and M. Varalatchoumy and R. C. Sreevidya and P. Prabhakar},

url = {https://www.sciencedirect.com/science/article/pii/S2451904925011254?via%3Dihub},

doi = {10.1016/j.tsep.2025.104334},

isbn = {24519049 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Thermal Science and Engineering Progress},

volume = {68},

publisher = {Elsevier Ltd},

abstract = {This paper provides a detailed experimental and machine learning analysis of peanut drying with a hybrid solar photovoltaic-thermal (PVT) collector system. Four drying techniques were tested: open sun drying, natural convection, forced convection, and forced convection combined with a paraffin wax-based Phase Change Material (PCM) to store latent heat. Each condition involved drying 5 kg of wet peanuts with an initial moisture content of 40 %. The drying processes were simulated using three machine learning models Gaussian Process Regression (GPR), Radial Basis Function (RBF), and Multilayer Perceptron (MLP) to predict moisture removal and drying performance. Model accuracy was measured by RMSE, MAPE, and R2. The results show that forced convection with PCM was the most successful approach, lowering drying time from around 42 h (open sun drying) to only 18 h and attaining the best drying efficiency of 68.23 %. The greatest electrical efficiency was 11.24 %, while the collector efficiency was 21.89 %. The RBF network outperformed the GPR and MLP models in moisture removal and drying performance, with R2 values of 0.97 and 0.98, respectively. This study concludes that integrating PCM with forced convection in a PVT-dryer system, together with powerful machine learning predictions, provides a highly efficient and sustainable approach for agricultural product preservation that outperforms existing techniques.},

note = {0},

keywords = {AIML},

pubstate = {published},

tppubtype = {article}

}

@article{688,

title = {Design and experimental validation of a compact inverted l-based quad-port MIMO antenna for 5G NR mm wave systems},

author = {A. K. Singh and A. K. Dwivedi and V. Singh and A. Pandey},

url = {https://www.nature.com/articles/s41598-025-25836-1},

doi = {10.1038/s41598-025-25836-1},

isbn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This paper presents a miniaturized (13x13x0.8 mm3) quad-port MIMO antenna for 5G NR n257, n258, & n261 for mm-wave usage. The recommended antenna designed at 29 GHz chose an optimal design (Stage 3) after a careful examination of performance at numerous stages. The single radiating plane consists of an inverted L-shaped design with identical hexagonal slotted loaded DGS. The quad components of the MIMO antenna are set orthogonally for best inter-element mutual coupling. The key feature of the recommended design is (a) impedance bandwidth of 25 -29.5 GHz (Simulated) and 25 to 27.5 GHz or 28 to 29 (measured), along with outstanding mutual coupling of > 25 dB suitable for new radio frequency (RF) bands n257, n258, & n261 (b) an impressive peak gain of 9.5 dB at a full band. Diversity parameters, such as the envelope correlation coefficient (ECC), diversity gain (DG), TARC and channel capacity loss (CCL) are analyzed and calculated the performance characteristics of the proposed MIMO antenna. The suggested antenna’s findings have been confirmed by experimental data and have been shown to be in close proximity to the simulated results.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{682,

title = {Neodymium Oxide Embedded Polypyrrole Matrix for Humidity Sensing Applications},

author = {L. B. Gunjal and S. Manjunatha and P. N. Shankar and A. Sunilkumar and Y. T. Ravikiran and H. L. Pushpalatha and N. M. Nagabhushana},

url = {https://www.tandfonline.com/doi/full/10.1080/25740881.2025.2560912},

doi = {10.1080/25740881.2025.2560912},

isbn = {2574089X (ISSN); 25740881 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Polymer-Plastics Technology and Materials},

volume = {64},

pages = {2701-2712,},

publisher = {Taylor and Francis Ltd.},

abstract = {In-situ chemical polymerization was employed to synthesize a polypyrrole matrix embedded with rare earth oxide, resulting in the formation of polypyrrole-neodymium oxide (PPy/Nd<inf>2</inf>O<inf>3</inf>) composites with varying weight percentages of Nd<inf>2</inf>O<inf>3</inf> (10, 30, and 50). The crystalline properties of these composites were elucidated through XRD spectra. The SEM images of the PPy/Nd<inf>2</inf>O<inf>3</inf> − 50% composite indicated a uniform distribution of Nd<inf>2</inf>O<inf>3</inf> within the PPy matrix, characterized by smaller grain sizes. Furthermore, TEM analyses corroborated the crystalline structure of the composite, confirming the incorporation of Nd<inf>2</inf>O<inf>3</inf> within the PPy matrix. FTIR spectra demonstrated the interfacial interactions between PPy and Nd<inf>2</inf>O<inf>3</inf>, as well as the composite’s formation. The composites’ capacity to detect humidity was assessed across a relative humidity spectrum of 10% to 97%. Notably, the PPy/Nd2O3–50% composite exhibited a rapid response time of 30 seconds and a recovery duration of 34 seconds, alongside commendable linearity (0.9991), sensitivity, and detection limits.},

note = {0},

keywords = {PHY},

pubstate = {published},

tppubtype = {article}

}

@article{681,

title = {Dual-port circularly polarized optically transparent MIMO antenna for N77/N78 5G applications},

author = {D. Smitha and B. R. Nayana and M. Pramukhya and M. Brunda and A. K. Dwivedi and V. Singh},

url = {https://www.cambridge.org/core/journals/international-journal-of-microwave-and-wireless-technologies/article/abs/dualport-circularly-polarized-optically-transparent-mimo-antenna-for-n77n78-5g-applications/C5D237C4A70825F9EB8AA7D86C1C316B},

doi = {10.1017/S1759078725102353},

isbn = {17590787 (ISSN); 17590795 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Microwave and Wireless Technologies},

publisher = {Cambridge University Press},

abstract = {A work of compact dual-port transparent multiple-input multiple-output antenna optimized for fifth-generation (5G) N77 (3.3–4.2 GHz) and N78 (3.3–3.8 GHz) bandwidth has been simulated, investigated, and optimized for robust performance in high-speed wireless communication. It features an impedance bandwidth of 3–4.3 GHz with a minimum simulated return loss of −28 dB, with 100% 3-dB axial ratio bandwidth and a simulated gain of 3.5 dB. The conducting plane material is indium tin oxide (ITO), chosen for its high optical transparency and sufficient electrical conductivity to seamlessly integrate into visually demanding applications. The substrate is glass, chosen for its lightweight and durable properties, which enhance both the mechanical durability of the antenna and its electromagnetic performance. To validate the ITO-based simulated design, the prototype with the same geometrical specification has been fabricated with the conducting portion replaced with copper and substrate as glass material due to a lack of facilities for transparent antenna fabrication. The comparative investigation study between the proposed ITO-based transparent antenna and with copper-based prototype (simulated/measured) both on a glass substrate, has been discussed, which supports the findings.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{677,

title = {Brain Tumor Detection from MRI Images Using Window-Aware Hierarchical Auto-Associative Polynomial Network with Great Wall Construction Algorithm},

author = {C. Selvan and B. Adusumalli and P. S. Geetha and Chrispin Jiji},

url = {https://link.springer.com/article/10.1007/s44174-025-00558-0},

doi = {10.1007/s44174-025-00558-0},

isbn = {27314820 (ISSN); 27314812 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Biomedical Materials and Devices},

publisher = {Springer Nature},

abstract = {Early brain tumor (BT) diagnosis is plagued by a small number of samples, limited generalizability to other groups, and a lack of ability to detect subtle variation in dense brain tissue. A new technique called Window-Aware Hierarchical Auto-Associative Polynomial Network with the Great Wall Construction Algorithm (WA-HAAPNet-GWCA) has been developed to overcome these problems. This method uses Magnetic Resonance Imaging (MRI) images from the CE-MRI (Contrast-Enhanced Magnetic Resonance Imaging) and Figshare datasets for assessment. Window-Aware Guide Filtering (WAGF) efficiently lowers noise and artifacts during preprocessing, improving image quality. R2U + + with Tuning Attention (R2U-TA) is used to locate tumors precisely. While the Scale-Aware Hierarchical Auto-Associative Polynomial Network (SA-HAAPNet) is used for feature extraction and classification, the Great Wall Construction Algorithm (GWCA) enhances model performance and reliability. The WA-HAAPNet-GWCA approach is able to use the CE-MRI and Figshare datasets and obtains a recall of 99.8% and accuracy of 99.9%, highlighting a very high diagnostic accuracy. The approach is efficacious and shows the feasibility of clinical use by providing solid answers to problems that have eluded diagnosis. This strategy, with elements drawn from the cutting edge, ensures cutting-edge performance in uncovering BTs at an early stage, which significantly enhances patient outcomes and contributes to the development of precision medicine.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{676,

title = {Efficient Breast Cancer Detection and Classification Using Rotation-Invariant Progressive Feedback Cosine Convolutional Neural Network with Tyrannosaurus Optimization Algorithm in Ultrasound Image},

author = {K. Garg and P. Prabu and M. G. Jayanthi},

url = {https://link.springer.com/article/10.1007/s44174-025-00539-3},

doi = {10.1007/s44174-025-00539-3},

isbn = {27314820 (ISSN); 27314812 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Biomedical Materials and Devices},

publisher = {Springer Nature},

abstract = {Breast cancer is the second most common cause of cancer-related deaths among women globally. Improved survival depends on a prompt and precise diagnosis, yet traditional ultrasound interpretation is time-consuming and error-prone. This study presents a new Rotation-Invariant Progressive Feedback Cosine Convolutional Neural Network (RiPFC-CNN-TOA) for the automatic and very effective diagnosis and classification of breast cancer in sonograms using the Tyrannosaurus Optimization Algorithm. First, UDIAT and BUSI dataset images are preprocessed with Fast Gradient Domain-Guided Image Filtering to improve contrast and eliminate noise while maintaining vital tumor features. Precise lesion segmentation is performed with a Machine learning-based Hybrid Mamba-Transformer model, capturing both spatial and sequential patterns of the images. For categorization, the RiPFC-CNN combines a Rotation-Invariant Attention Network and a Progressive Feedback Cosine CNN for feature-rich and orientation-invariant feature extraction. By modeling intelligent predator–prey behavior and fine-tuning weights, the Tyrannosaurus Optimization Algorithm also enhances the model's performance. The suggested approach obtains excellent performance: accuracy of 99.53% (BUSI) and 99.6% (UDIAT), precision over 99.3%, and large F1-scores and specificity, greatly enhancing diagnostic reliability with a significant reduction in false positives. This framework provides a robust, clinician-friendly algorithm for early breast cancer identification with excellent potential for real-world application in diagnostic pipelines.},

note = {0},

keywords = {CSE},

pubstate = {published},

tppubtype = {article}

}

@article{675,

title = {INFLUENCE OF COBALT CONTENT ON THE MECHANICAL AND EROSION PERFORMANCE OF HVOF-SPRAYED WC-Co COATINGS ON INCONEL 718 SUPERALLOY},

author = {U. Harish and M. V. Kumar and M. Mruthunjaya and M. V. Mohan and C. Chitrada and B. S. Bindhushree and P. K. Soni and C. H. Kumar and H. Soni and C. D. Prasad},

url = {https://www.worldscientific.com/doi/10.1142/S0218625X26500125},

doi = {10.1142/S0218625X26500125},

isbn = {17936667 (ISSN); 0218625X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Surface Review and Letters},

publisher = {World Scientific},

abstract = {This study presents a detailed analysis of the microstructure and erosion behavior of WC-Co composite coatings with different cobalt levels: WC-10Co, WC-13Co, and WC-16Co, applied on nickel-based Inconel 718 steel superalloy using the HVOF spray process. The research shows that the coatings’ hardness increases with cobalt content, with WC-16Co reaching the highest microhardness of 825 Hv. XRD analysis indicates that the WC-16Co coating displays notably sharper cobalt peaks as the cobalt content increases, indicating improved phase visibility. This correlates with a distinct layered structure exhibiting minimal porosity of 1.1±0.15%, compared to 1.4±0.20% for WC-10Co and 1.3±0.15% for WC-13Co. The increased hardness and lower porosity of WC-16Co suggest better wear and erosion resistance relative to the other coatings. Overall, this study emphasizes the impact of cobalt content on the microstructural and mechanical properties of WC-Co coatings, identifying WC-16Co as the most durable option for applications demanding superior erosion resistance.},

note = {0},

keywords = {CIVIL},

pubstate = {published},

tppubtype = {article}

}

@article{674,

title = {REOUN: restoration and enhancement of optical imaging underwater based on non-local prior},

author = {Chrispin Jiji and M. S. Sujitha and Annie Bessant and G. Indumathi},

url = {https://link.springer.com/article/10.1007/s12596-024-02097-1},

doi = {10.1007/s12596-024-02097-1},

isbn = {09746900 (ISSN); 09728821 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Optics (India)},

volume = {54},

pages = {1837-1850,},

publisher = {Springer},

abstract = {The complex aquatic environment of underwater imaging sometimes leads to significant image distortion. Due to light absorption and dispersion in aqueous medium, underwater photographs frequently experience serious quality loss, including poor visibility, contrast reduction, and colour divergence. Reducing colour cast, increasing contrast, and improving visibility in these photographs is a difficult task. To increase the quality of underwater photos, restoration and enhancement based on Non-Local previous technique has been developed. However, the utilisation of several undersea image restoration and enhancement techniques is hampered by the over- or under-enhancement they yield. Initially, the use of Non local prior averages pixel intensities of various locations spread across the hazy image plane have a quasi-linear association with those over the equivalent haze free images. Secondly, a dual optimization function is employed to reduce the size of the solution space to remove blur. Furthermore, by estimating the imaging parameters using the information present in the full image, this unique dual optimisation technique produces a more dependable restoration. Thirdly, the negative interference brought about by the region segmentation is removed by the use of a gradient filter. Finally, an improved weighted grey edge method is adopted to enhance image brightness and visibility. A comparison is made between the REOUN and an existing methods in terms of both objective and subjective visual impact. We compared the edge information of the restored results since texture and details are crucial to images and serve as evaluation criteria to gauge an image’s performance. Comparison results show that the suggested REOUN retains the edges best over other techniques.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{672,

title = {A Framework for Enhanced Crop and Fertilizer Recommendations Using Machine Learning, Explainable AI, and RPA},

author = {J. P. Kumar},

url = {https://inass.org/wp-content/uploads/2025/07/2025113017.pdf},

doi = {10.22266/ijies2025.1130.17},

isbn = {21853118 (ISSN); 2185310X (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {International Journal of Intelligent Engineering and Systems},

volume = {18},

pages = {262-274,},

publisher = {Intelligent Network and Systems Society},

abstract = {This work explores the challenge of crop and fertilizer prediction by leveraging machine learning and AI to enhance agricultural practices, particularly for small-scale farmers in India. A major contribution in this research is the use of Robotic Process Automation (RPA) based on the UiPath platform, which enhances the accuracy and efficiency of data collection. The basis of this research puts forward a new ensemble technique that combines XGBoost and Random Forest, which demonstrates better predictive accuracy than traditional models and other ensemble techniques. The proposed framework achieved an accuracy of 98.0% and an F1 score of 98.2% on crop and fertilizer recommendation tasks, outperforming baseline models by over 3%. These results demonstrate the model’s effectiveness and reliability in real-world agricultural scenarios. We present an integrated framework that combines these ensemble techniques with Explainable AI (XAI). This technique ensures that the generated predictions are explainable, thus making it possible for stakeholders to understand and accept the recommendations offered.},

note = {0},

keywords = {CSE},

pubstate = {published},

tppubtype = {article}

}

@article{671,

title = {A high isolated, high gain millimeter wave quad-port MIMO antenna array for wideband 5G new radio application},

author = {P. Tiwari and J. K. Rai and A. K. Dwivedi and V. Gahlaut and P. Ranjan and R. Chowdhury and A. Pandey},

url = {https://www.nature.com/articles/s41598-025-18805-1},

doi = {10.1038/s41598-025-18805-1},

isbn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

publisher = {Nature Research},

abstract = {This paper introduces a high-performance quad-port Multiple-Input Multiple-Output (MIMO) antenna array for wideband 5G New Radio (NR) (n258, n257, n260, and n261) applications in the millimeter-wave (mmWave) spectrum. The target antenna structure employs a 1 × 2 element array design with microstrip line feeding and includes diamond-shaped slots along with defected ground geometry (DGG) to increase bandwidth, gain, and isolation independent of complicated decoupling components. Fabricated on low-loss Rogers RT/Duroid 5880 substrate (εr = 2.2, tanδ = 0.0009), the antenna has an ultrawide operation bandwidth of 24 GHz to 40 GHz with a 50% fractional bandwidth. The measured results show a peak gain of 18.2 dBi and outstanding inter-element isolation of over 44 dB, confirming the potential for interference-free MIMO operation of the antenna. The design also exhibits better diversity performance with an ECC of < 0.0005, diversity gain (DG) of 9.99 dB, and a CCL of < 0.3 bps/Hz, affirming its use in high-capacity, low-latency 5G mmWave systems. In addition, the compact form factor of the antenna (35 × 35 × 0.254 mm3), structural simplicity, and strong performance make it extremely suitable for integration within next-generation IoT platforms, indoor/outdoor wireless systems, and flexible mmWave-enabled communication devices. The present work aims to overcome challenging problems in the realization of wideband operation, high gain, and excellent isolation in compact MIMO configurations and presents a scalable and fabrication-friendly solution for future wireless networks.},

note = {0},

keywords = {ECE},

pubstate = {published},

tppubtype = {article}

}

@article{670,

title = {Simplicial reflection graph equivariant secretary bird quantum attention networks model for evaluating teaching quality in higher education},

author = {C. Selvan and Santha Kumari and M. Shanmugathai and S. Shankar},

url = {https://link.springer.com/article/10.1007/s13278-025-01503-1},

doi = {10.1007/s13278-025-01503-1},

isbn = {18695469 (ISSN); 18695450 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Social Network Analysis and Mining},

volume = {15},

publisher = {Springer},

abstract = {Evaluating teaching quality in higher education is vital to ensuring academic excellence and student learning outcomes. But the varied, subjective, and frequently variable nature of assessment indicators makes it a challenge to measure them effectively. In order to address these issues, this research proposes a novel model for evaluating the quality of instruction in higher education using simplicial reflection graph equivariant secretary bird quantum attention networks (SRGE-SBQAN). "Teaching Quality Evaluation in Higher Education," a dataset of 100 records that cover a wide range of teaching attributes from Indian universities, is used to train and evaluate the model. The data preprocessing phase utilizes Correlation Coefficients and Min–Max Normalization to normalize feature scales and minimize noise. Feature extraction is conducted via a Maximum-Entropy Regularized Decision Transformer, which detects the most impactful teaching characteristics. Reflection-Equivariant Quantum Neural Networks (REQNN) and Simplicial Graph Attention Transformers (SGAT) are used in the SRGE-SBQAN model to produce dependable predictions. The Secretary Bird Optimization Algorithm (SBOA) is used to adjust the model's parameters. The suggested model performs exceptionally well in terms of accuracy (99.9%), precision (99.5%), recall (99.7%), specificity (99.2%), F1-score (99.1%), Mean Absolute Error (MAE) (3.7), and Root Mean Square Error (RMSE) (3.3). According to these findings, the model is a powerful instrument for data-driven analysis and quality enhancement in higher education because of its strong generalization capacity, scalability to huge data, and superior interpretability.},

note = {0},

keywords = {MECH},

pubstate = {published},

tppubtype = {article}

}

@article{647,

title = {Green Synthesis of Silver-Doped NiO Nanoparticles for Photocatalytic and Electrochemical Sensor Applications from Neem Leaf Latex},

author = {Naveen Kumar and M. Balakrishna and S. Sudhakar and U. Desai and N. Naik},

url = {https://link.springer.com/article/10.1007/s11244-025-02190-0},

doi = {10.1007/s11244-025-02190-0},

isbn = {10225528 (ISSN); 15729028 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Topics in Catalysis},

publisher = {Springer},

abstract = {Neem (Azadirachta indica) leaf extract was used to create pure NiO and Ag-doped NiO nanoparticles (Ag-NiO NPs) of 1, 3, and 5 mol% by solution combustion method at 400 °C and calcining at 1000 °C for two hours. The synthesized Ag-NiO NPs were comprehensively characterized using various analytical techniques, including X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM/HRTEM), Selected Area Electron Diffraction (SAED) and UV-diffuse reflectance spectroscopy (UV-DRS). Powder X-ray diffraction (PXRD) analysis confirmed their cubic crystal structure with a space group of Fm-3m. The average crystallite size of 25 nm. The bandgap energy for pure NiO, 1%, 3%, and 5% Ag doped NPs is to be 4.08, 4.05, 3.96, and 3.84 eV. The 3% Ag-doped NiO showed strong photocatalytic activity in breaking down Fast Blue (FB) and Acid Orange Red (AOR) dyes, with absorption peaks at 622 nm and 484 nm, respectively. After 120 min of UV light exposure, a significant reduction in dye concentration was observed, confirming the material’s effectiveness for photocatalytic applications. Excellent redox potential output was determined by cyclic voltammetry (CV) in an electrochemical evaluation of the produced material using graphite electrode paste in 1 M KOH electrolyte. Electrochemical impedance spectroscopy (EIS) research has demonstrated that the decreased charge transfer resistance of Ag-NiO NPs is responsible for their improved behaviour. After computation, it was discovered that the Ag-NiO NPs -specific capacitance values were 125.7 Fg−1 at a scan rate of 50 mVs−1. The paracetamol and penegra materials were effectively sensed by the prepared carbon paste electrode of Ag-NiO NPs (3 mol%) as revealed by cyclic voltammetric and EIS studies. All the above results show that the synthesized Ag-NiO (3%) NPs can be a potential candidate for waste water treatment and electrochemical sensor applications.},

note = {0},

keywords = {Cyclic voltammetry, PHY},

pubstate = {published},

tppubtype = {article}

}