Journal Articles
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Item XPM-induced crosstalk with higher order dispersion in SCM–WDM optical transmission link(Elsevier GmbH journals@elsevier.com, 2012) Kumar, N.; Sharma, A.K.; Kapoor, V.In this paper, the XPM-induced crosstalk has been evaluated with higher order dispersion in SCM–WDM optical transmission link at different modulation frequencies. It has been observed that there is exponent increase in XPM-induced crosstalk with the increase in modulation frequency from 0 to 3 GHz. The impact of 3OD, 4OD and 5OD is small as compared to 2OD but still contributes when the combined terms are considered. The combined effect of second, third, fourth and fifth-order dispersion parameters is that the induced crosstalk introduced by XPM increases. © 2011 Elsevier GmbHItem Investigating the performance of electromagnetic pump fabricated using tool based micromachining setup for microdelivery of fluid(Bangladesh University of Engineering and Technology, 2019) Veeresha, R.K.; Karegoudra, M.K.; Rao, M.; Rao, R.; Kumar, N.Micropumps play an important role in the delivery of insulin, hormonal and pain management for biomedical application. The main reason for the micropump is to pump a small amount of fluid to the target area and also control the pumping fluid. Electromagnetically operated pump fabricated using tool-based micromachining setup for the micro-delivery of the fluid. The electromagnetic pump was supplied with an input voltage of 6V-12V and at different frequencies starting from 1Hz to 5Hz with the increment of 1Hz. The maximum head developed is at a frequency of 3Hz which the optimum frequency for this configuration of an electromagnetic pump is. The maximum head obtained at this optimal frequency is 25mm. Finally, in order to measure the flow rate of the electromagnetic pump the pump was actuated at 3Hz frequency alone by varying the head of the micropump from 4 to 20mm. © 2019 Bangladesh University of Engineering and Technology. All rights reserved.Item Poly(N,N-diethyl acrylamide)/functionalized graphene quantum dots hydrogels loaded with doxorubicin as a nano-drug carrier for metastatic lung cancer in mice(Elsevier Ltd, 2019) Havanur, S.; Batish, I.; Cheruku, S.P.; Gourishetti, K.; JagadeeshBabu, J.; Kumar, N.Cancer has emanated as a daunting menace to human-kind even though medicine, science, and technology has reached its zenith. Subsequent scarcity in the revelation of new drugs, the exigency of salvaging formerly discovered toxic drugs such as doxorubicin has emerged. The invention of drug carrier has made drug delivery imminent which is ascribable to its characteristic traits of specific targeting, effective response to stimuli and biocompatibility. In this paper, the nanoscale polymeric drug carrier poly(N,N-diethyl acrylamide) nanohydrogel has been synthesized by inverse emulsion polymerization. Lower critical solution temperature of the polymeric carrier has been modified using graphene quantum. The particle size of pure nanohydrogel was in the range of 47 to 59.5 nm, and graphene quantum dots incorporated nanohydrogels was in the range of 68.1 to 87.5 nm. Doxorubicin (hydroxyl derivative of anthracycline) release behavior as a function of time and temperature was analyzed, and the Lower critical solution temperature of the synthesized nanohydrogels has been found to be in the range of 28–42 °C. Doxorubicin release characteristics have improved significantly as the surrounding temperature of the release media was increased near to physiological temperature. Further, the cumulative release profile was fitted in the different kinetic model and found to follow a Fickian diffusion release mechanism. The hydrogel was assessed for its cytotoxicity in B16F10 cells by MTT assay. In-vivo studies were done to study the lung metastasis by melanoma cancer and the results showed a rational favorable prognosis which was confirmed by evaluating hematological parameters and the non-immunogenic nature of nanohydrogel by cytokine assay. Comprehensively, the results suggested that poly(N,N-diethyl acrylamide) nanohydrogels have potential application as an intelligent drug carrier for melanoma cancer. © 2019 Elsevier B.V.Item Analysis and prediction of COVID-19 trajectory: A machine learning approach(John Wiley and Sons Ltd, 2021) Majhi, R.; Thangeda, R.; Sugasi, R.P.; Kumar, N.The outbreak of Coronavirus 2019 (COVID-19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. Regression-based, Decision tree-based, and Random forest-based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real-time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown. © 2020 John Wiley & Sons LtdItem Numerical study on the effect of steel fibers on fracture and size effect in concrete beams(Elsevier Ltd, 2023) Yadav, D.; Prashanth, M.H.; Kumar, N.The construction sector uses concrete extensively all around the world. Concrete contains a lot of microcracks even before it is loaded. When a tensile force is applied, these microcracks attempt to open up. While designing, the strength of concrete in its tensile zone is ignored. The strength and ductility of the concrete can be improved due to the addition of steel fibers. Steel fibers use a bridge mechanism to restrict the micro-cracks spread. This study uses ABAQUS to numerically analyze the behaviour of the Steel Fiber Reinforced Concrete (SFRC) beams. Two grades of concrete are studied, M20 and M60, for varying volumetric percentages of steel fibers. It was observed from the study that the ultimate load increases by around 52% and 41% for M25 and M60 grade concrete, respectively, by adding 1% of steel fiber. Fracture properties such as fracture toughness and fracture energy are calculated. The addition of steel fibers enhanced fracture toughness and energy significantly. Adding 1% fiber increases fracture toughness by around 56% and 34% and fracture energy by around 169% and 136% for M25 and M60 concrete, respectively. The size effect on SFRC beams is studied to determine the size-independent fracture parameters. © 2023Item A neural network-based predictive decision model for customer retention in the telecommunication sector(Elsevier Inc., 2024) Thangeda, R.; Kumar, N.; Majhi, R.Acquiring a new customer is far more expensive than retaining a customer. Hence, customer retention is a key aspect of business for a firm to maintain and improve on its market share and profit. The paper analyses customer retention strategies by employing an artificial neural network-based decision model to a real-life dataset collected from 311 mobile service users in India. Seven linear and non-linear adaptive models are developed using features related to customer dissatisfaction (DSF), customer disloyalty (DLF) and customer churn (CF). Findings of this study suggest that non-linear models are most efficient in predicting customer churn, and both DSF and DLF variables significantly affect the retention strategy. Three groups of customers are discussed in this study in the order of least likelihood of churning to most likelihood. Finally, a priority matrix based on key performance indicators is proposed to help service providers target potential customers to retain. © 2024 The AuthorsItem Intelligent GD&T symbol detection in mechanical drawings: a comparative study of YOLOv11, Faster R-CNN, and RetinaNet for quality assurance(Springer, 2025) Narendra Reddy, T.N.; Kumar, N.; Ponnappa, N.P.; Mohana, N.; Vinod, P.; Herbert, M.A.; Rao, S.S.Geometric dimensioning and tolerancing (GD&T) symbols play a vital role in engineering drawings by specifying allowable variations in part geometry to ensure manufacturing precision and functional performance. Manual identification and extraction of these symbols is labour-intensive, prone to human error, and increasingly unsuitable for fast-paced production environments, as it significantly increases quality inspection time and indirectly delays overall product delivery. This research is specifically conducted to support the development of intelligent quality management systems by integrating machine learning algorithms capable of detecting GD&T symbols directly from CAD-generated mechanical drawings. Such capability is essential for automating inspection processes and enabling reliable data extraction from design files, which are foundational to digital manufacturing workflows. Additionally, with many commercial quality automation tools being prohibitively expensive for small and medium-sized enterprises (SMEs) and micro, small, and medium enterprises (MSMEs), there is a pressing need for cost-effective, indigenous solutions. This study addresses that gap by evaluating three state-of-the-art deep learning-based object detection models—YOLOv11, Faster R-CNN, and RetinaNet—for GD&T symbol recognition. Each model was trained on a custom dataset annotated with diverse GD&T symbols, and performance was assessed using standard evaluation metrics: accuracy, recall, F1 score, and inference speed. The results show that while all three models demonstrate robust performance, YOLOv11 strikes the best balance between detection accuracy and real-time execution. This comparative study not only guides R&D teams in selecting the most suitable model for quality automation tasks but also contributes to the broader goal of enabling affordable, scalable, and intelligent visual inspection systems for SMEs and MSMEs. © The Author(s) 2025.Item Electro-discharge machining of microholes on 3d printed Hastelloy using the novel tool-feeding approach(KeAi Publishing Communications Ltd., 2025) Korgal, A.; Shettigar, A.K.; P, N.K.; Kumar, N.; Bindu Madhavi, B.M.Hastelloy, a nickel-based superalloy renowned for its exceptional resistance to corrosion at high temperatures, is widely used in sectors such as nuclear, aerospace, chemical processing, and pharmaceuticals. Microelectrical discharge machining (?-EDM) is crucial for generating microholes and channels on Hastelloy. Since it effectively addresses difficulties like work hardening, high strength & wear resistance, and low thermal conductivity in traditional machining. Microholes play a major role in many critical components for precise control of fluids in fuel injectors, managing heat in turbine blades, controlled gas exchange, etc. The current research investigates the drilling of 8:1 aspect ratio microholes machined by 400 ?m diameter electrodes. This study investigated the influence of tool material (tungsten carbide, carbide drill bit, and brass) on ?-EDM performance. Compared to tungsten carbide and carbide drill bits, brass exhibited significantly lower electrode wear, leading to more precise microholes with reduced overcut and taper angle. However, brass also required a substantially longer machining time. Carbide drill bits offered a balance between wear resistance, machining time, and overcut/taper angle. © 2024 The Authors