University College of Engineering Arni, a constituent college of Anna university, was founded in 2009 at Thatchur located near to Arani, town of Thiruvannamalai District. The total annual intake for first year UG programme is 300 seats..
This study presents a novel non-invasive glucose biosensor based on a multilayer Kretschmann-configured surface plasmon resonance (SPR) configuration enhanced with two-dimensional and nanostructured plasmonic materials. The proposed sensor consists of a BK7 prism, a monolayer of tungsten disulfide (WS2), trilayer graphene, a silver (Ag) thin film and a carbon nanotube (CNT) overlayer, strategically designed to achieve strong plasmon-material coupling and enhanced electromagnetic field confinement at the sensing interface. The biosensor is designed for quantitative detection of glucose concentrations in urine samples over the clinically relevant range of 0-10 g/dL. A comprehensive electromagnetic analysis based on the transfer matrix method (TMM), supported by finite-element simulations using COMSOL Multiphysics, is employed to evaluate the optical response of the proposed design. The present investigation is purely numerical and all reported results are obtained through theoretical modeling and finite-element simulations, thereby providing a reliable performance benchmark for future experimental realization. The sensor exhibits a maximum local angular sensitivity of 400 degrees/ RIU and an average sensitivity of approximately 220 degrees/RIU over the investigated refractive index range and a figure of merit (FOM) of 59.7 RIU-1. A distinct resonance angle shift from 71.4 degrees to 73.4 degrees is observed for refractive-index variations from 1.335 to 1.347 RIU, enabling reliable discrimination between normal and diabetic glucose levels. Comparative analysis confirms that the WS2-graphene-CNT hybrid configuration demonstrates competitive and improved performance compared to previously reported multilayer SPR biosensors in terms of both sensitivity and FOM. With its high detection precision, strong plasmonic enhancement and noninvasive label free measurement capability, the proposed SPR biosensor provides a promising simulationvalidated design for diabetes monitoring, supporting SDG 3 (Good Health and Well-Being) andproviding a foundation for future experimental and clinical investigations.
Lung cancer is the most dangerous disease in the world, leading to high mortality in daily life. So, the early detection of this disease is essential to enhance the survival rate of patients worldwide. However, the small extent of lung nodules is not easily found by eye vision, so this leads to the inaccurate diagnosis of lung cancer. Nowadays, deep neural systems technology-based machine learning has been widely used in healthcare units to diagnose various diseases. It is one of the best ways to classify subtle parts of a CT scan exactly. Therefore, proposed a Sailfish-based Yolo Segmentation Framework (SbYSF) that aimed to segment the lung nodule part accurately. Initially, the CT image datasets were collected preprocessed, and required features were extracted using the sailfish function. The nodule region is traced through the extracted features and segmented. Further, the severity is attained using the SbYSF framework. The planned model accurately detected the nodule region for the early stage of a lung cancer diagnosis. The proposed model is checked in the Python simulation environment. They reached the highest accuracy, precision, and recall of 99.75% and F-measure of 99%.
The current research presents a compact, novel hexa-band quad-patch MIMO antenna designed for 5G millimeter-wave (mmWave) systems. The antenna, measuring 52 mm × 52 mm, is built on a Rogers RT/duroid 5880 (tm) dielectric substrate with a relative permittivity of 2.2, thickness of 0.8 mm, and dielectric loss tangent of 0.0009. The proposed antenna is made up of quad rectangular stepped extension slotted radiating elements (patches) on top of an 8-arm star-shape defected substrate and a plus shaped partial ground plane at the bottom. A microstrip line feed of 50 ohms is used to excite the radiating components. To enhance the isolation properties of MIMO antennas, optimized crossed stubs are employed as a decoupling structure between monopole radiators. The antenna generates six important frequency bands at 21.5 GHz, 28 GHz, 32 GHz, 34.5 GHz, 44 GHz, and 49.5 GHz with maximum reflection coefficient is -36 dB, and transmission coefficient (isolation) of greater than 22.5 dB throughout all bands. The developed antenna has a high peak gain of 10.73dBi, radiation efficiency of 98.3
Air quality monitoring is important for environmental management, especially to forecast PM2.5-level pollution concentration. Classical DL models involve heavy computational power, inefficient task offloading, and poor adaptability. Hence, it is ill-fitted for real-time edge application scenarios. To this end, Multi-objective Artificial Afterimage deep Q Self-Attention and Inter-sample Attention Transformer (MAAQ-SAINT) is proposed for PM2.5 prediction on edge devices such as Raspberry Pi 4B and 3B + . The design implements Regression Relief Feature Selection (RRFS) for optimal feature extraction, and Optimal Stopping Theory (OST) for task offloading. The Multi-objective Artificial Afterimage Algorithm (MAAA) is used to enhance prediction accuracy while reducing computational complexity. A self-attention mechanism and inter-sample attention mechanism learn spatial and temporal dependencies by maintaining robust performance. Quantization represents applied optimization for processing in resource-constrained environments. A performance evaluation using MAE, RMSE, and execution time shows that MAAQ-SAINT outperforms traditional techniques in terms of classification (prediction) accuracy (99
The size and site of distribution generation (DG) integration in the distribution power network (DPN) are critical for power loss (PL) reduction, bus voltage (BS) improvement, and stability enhancement. This chapter optimizes type I DG (photovoltaic system) into the radial DPN via the application of the osprey algorithm (OA). The OA technique optimizes DG's site and size to minimize the real PL and voltage deviation (VD) of radial DPN. The VD minimization improves the VP of DPN. The efficacy of the OA-optimized DG placement is investigated on a balanced radial DPN with 33 buses. OA-optimized PV system inclusion has minimized the PL of the 33-bus radial DPN by 53.23
