CMR Institute of Technology

The effective detection and monitoring of various pathogens remain critical for controlling outbreaks and ensuring timely medical intervention. To address this issue, the current work proposes a surface plasmon resonance (SPR) sensor based on Kretchman configuration for precise detection of change in refractive index of pathogens related to dengue diseases by studying the different blood components like plasma, platelet, and hemoglobin. The sensor is envisaged by stacking layers of ${\text{TiO}}_{2}$, Ag, AlN, graphene, and sensing medium on a BK7 prism substrate. The structure is designed and modeled in COMSOL Multiphysics to clearly demonstrate the excitation of plasmonic wave close to the metal surface. The thicknesses of all the constituent layers have been meticulously optimized by studying the change in characteristics of the angular reflectance. Notably, the penetration of evanescent field into the sensing medium enhances the light-analyte interaction, which leads to high sensitivity. Moreover, a detailed electric field analysis is carried out at the interface of each layer. The simulation upshots revealed that the proposed sensor can detect infected plasma, platelet, and hemoglobin with a sensitivity of 138.46 deg./RIU, 163.63 deg./RIU, and 182.85 deg./RIU, respectively. This approach offers significant advantages, including rapid detection of pathogens, high sensitivity, and the ability to integrate with portable diagnostic devices, making it a promising tool in the biomedical industry. Graphical Abstract

Deepfake manipulation of MRI brain tumor scans pose significant risks, including misdiagnosis, fraudulent research, and ethical concerns in medical imaging. To address this, we propose a Video Vision Transformer (ViViT)-based deepfake detection model enhanced with landmark-based feature extraction, designed to identify manipulated MRI scans with high accuracy. Our approach adapts ViViT to analyze sequential MRI slices, capturing subtle inconsistencies introduced by deepfake techniques, while landmark-based features focus on key tumor regions for improved precision. Additionally, Depthwise Separable Convolutions (DSC) and Convolutional Block Attention Module (CBAM) are incorporated to enhance feature learning and computational efficiency. The proposed model is evaluated on the BraTS 2021 dataset, achieving a Dice Similarity Coefficient (DSC) of 91.2%, Intersection over Union (IoU) of 85.7%, and an AUC-ROC of 98.4%, significantly outperforming CNN-based and GAN-forensic approaches. Based on the results, the proposed system exhibit higher accuracy and robustness.

The increasing adoption of DC microgrids, driven by the integration of renewable energy sources and the need for efficient power systems, necessitates advanced fault detection mechanisms. Traditional fault detection methods, such as Fourier Transform (FT) and Discrete Fourier Transform (DFT), are limited by their assumptions of signal stationarity and inadequate time-localization capabilities, particularly in detecting high-resistance faults. This research paper investigates the integration of Long Short-Term Memory (LSTM) networks with the Hilbert-Huang Transform (HHT) model to address these limitations. The proposed LSTM-HHT approach leverages LSTM’s ability to capture long-term dependencies and time-series patterns, along with HHT’s proficiency in analysing non-linear and non-stationary signals. The integrated model is implemented and tested using MATLAB Simulink to evaluate its performance in practical DC microgrid scenarios. Results demonstrate that the LSTM-HHT approach significantly enhances fault detection accuracy and reliability, particularly for high-resistance faults that are challenging to identify with traditional methods. The empirical validation in simulated environments highlights the model’s effectiveness in accurately detecting and localizing faults, thereby improving the stability and safety of DC microgrids. This research contributes to the development of more resilient and intelligent fault detection systems, supporting the broader adoption of DC microgrids and the transition to sustainable energy systems. The findings underscore the potential of combining advanced signal processing techniques with machine learning to overcome the inherent limitations of conventional fault detection methods, paving the way for further innovations in microgrid management.

This research explores a modified photonic crystal structure called the annular photonic crystal (APC) for enhanced gamma ray detection, specifically in the range of 0 Gy to 70 Gy. The APC design is primarily based on porous silicon and a polyvinyl alcohol (PVA) polymer that is doped with crystal violet (CV) and carbol fuchsine (CF). The selection of these materials is motivated by their significant changes in refractive index when exposed to gamma ray doses. Detection relies on the appearance of a resonant peak in the reflectance spectrum of the structure, which arises from a defect layer created by the PVA polymer doped with CV and CF dyes at the center of the structure. To analyze the variations in the defect mode characteristics within the reflectance spectrum at different gamma ray doses, a modified transfer matrix method is utilized. Various geometric parameters of the structure are meticulously optimized to achieve optimal sensing performance. This hybrid structure enhances the interaction efficiency between the incoming radiation and the photonic crystal matrix, resulting in a notable sensitivity of 227.19 nm/RIU. Additionally, the proposed sensor is easy to fabricate and can be readily integrated with other photonic devices, making it an ideal candidate for dosimetry applications in medical treatments, radiation protection, and industrial processes.

Cancer remains one of the leading causes of mortality worldwide, with breast cancer being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment; however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, non-invasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for bio-sensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for breast cancer detection, the identification and analysis of MCF-7 breast cancer cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multi-band metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 breast cancer cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminum (Al) layers on a 50-μm-thick polyethylene terephthalate (PET) substrate. With dimensions of only 198 × 198 μm², the proposed device is exceptionally compact. It operates in the 0.5 THz to 1.6 THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating electric and magnetic field distributions, surface currents, and scattering parameters. Extensive benchmarking demonstrates the device’s superior performance compared to state-of-the-art biosensors, excelling in metrics such as quality-factor, figure of merit (FOM), and absorption efficiency. Additionally, the proposed sensor has been integrated into an MWI system to evaluate its practical application. The device successfully discriminated against subtle changes in the refractive index of biological tissues, confirming its ability to detect MCF-7 cells effectively. These findings highlight the sensor's suitability as a non-invasive, early-stage diagnostic tool for breast cancer.

The gold market is highly volatile and is considered the safest investment during economic uncertainties and inflationary pressures, and one must know its price movement. It is essential to know their prices because they can swing with the global financial markets around. Accurate prediction models allow the realization of profit based on insights derived from the model. The current study compares machine learning algorithms against econometric models in predicting gold prices in India. The efficiency of the models is evaluated based on RMSE, R-Square, AIC, BIC, and MAPE. Internal and external factors that affect the gold price were chosen for the study, they are historical gold prices, the demand and supply, exports and imports of gold, inflation, interest rates, the exchange rates (USD/INR), gold reserves, GDP, and BSE Sensex. Data is gathered for the period ranging from January 2010 to December 2023. From the analysis, it is evident that the econometric model outperforms traditional models in predicting gold prices.

Scheduling and routing problems for maximizing the lifetime of wireless sensor networks (WSN) have been well studied. WSNs provide many previously described algorithms to support lifetime maximization and node scheduling. However, it is difficult to achieve a higher performance to maximize the lifetime of sensor nodes. Among these, the additive weight-based dynamic scheduling algorithm (HAWDS) adjusts the weights according to the average queue size and adaptively changes the weights of the scheduling scheme in a manner that favours premium services. This study explores an active queue buffer management technique for efficiently handling the cluster head (CH) buffer queues. This approach involves dynamically allocating the CH buffer size to neighboring nodes based on the quantity of received packets, thereby mitigating the risk of packet loss. Additionally, this research examines the Acknowledgement encounter strategy, which evaluates two methods of acknowledgment delivery: explicit and implicit. Substantial advancements were observed across all evaluation criteria. The study revealed a 42% increase in the packet delivery ratio (PDR), an 87% enhancement in throughput, and an 86% improvement in scheduling effectiveness. The proposed framework outperformed MCAR in terms of energy efficiency by 13.33% and extended network durability to 98%, exceeding DHRP by 4.87% and EEBS by 8.86%. Illustrating its effectiveness, the proposed method delivered outstanding results: 98.4% PDR, 8300 packets throughput, 98% scheduling efficiency, and 98% network durability. These metrics highlight the superior performance and operational excellence of the framework.

Recent research has shown that copper nanoparticles can cure and even eradicate the most severe disease called cancer. Due to their large atomic number and massive heat output, these particles are useful in the treatment of malignant tumors. Given this research, this work aims to present the findings from an intensive study on the peristaltic transport of Phan–Thien–Tanner fluid driven by the Lorentz phenomenon carrying the nanoparticles of copper in an asymmetric channel, with a focus on the entropyanalysis and therapeutic implementation. Considering the case of long wavelengths, low Reynolds numbers, heat radiation, and Hall effects, the double‐diffusive convection of the Phan–Thien–Tanner MHD peristaltic flow with thermophoresis and Brownian motion of copper nanoparticles is studied. The governing equation is non‐dimensionalized, utilizing parameters with no dimensions. By employing the perturbation method, the model's governing equations are solved. It is important to note that the velocity of the fluid is presumed to be affected by the magnetic field. Also, the nanofluid's thermophoresis parameter increases the temperature of the fluid. Therefore, to transport drugs and treat cancer, magnetic fields and nanoparticles are used. The Hall parameter increases the flow characteristics of the fluid, while the Weissenberg number shows dissimilar behaviors. Furthermore, the present study is checked with the previous work via the plot, indicating that the two reports accord well.

An electrochemical sensor with excellent sensitivity has been developed for the continuous and selective identification of (DA) dopamine and (5-HT) serotonin via a platinum (pt) - doped reduced graphene oxide nanocomposite (Pt-doped rGO). The sensor utilizes the synergistic properties of its components: the increased surface area and electrical conductivity of rGO, the improved electron transfers due to platinum doping, and the structural benefits of the composite for efficient neurotransmitter detection. The Pt-doped rGO nanocomposite is produced by directly oxidizing graphite to generate graphene oxide (GO), subsequently reducing and functionalizing GO with platinum nanoparticles. Electrochemical characterization using differential pulse voltammetry (DPV) demonstrated clear separation of oxidation peaks for DA and 5-HT, allowing precise multiplexed detection. The sensor demonstrated superior electrocatalytic activity, selectivity, and no interference from ascorbic acid (AA), frequently found in electrochemical biosensing. The detection limits were 0.012 µM for both dopamine (DA) and serotonin (5-HT). The analysis of actual samples in human urine and serum validated the sensor’s practicality and reproducibility. The Pt-doped rGO composite effectively tackles significant issues in electrochemical biosensing, such as overlapping redox potentials and interference from intricate biological matrices, rendering it a promising platform for the highly sensitive and selective detection of neurotransmitters.

This study presents an advanced analytical model for the InP/GaN-graphene channel surrounding gate tunnel field-effect transistor (SGT-TFET) for next-generation biosensing applications. The proposed device leverages indium phosphide (InP) and gallium nitride (GaN) as the source and drain materials, respectively, while graphene serves as a highly conductive channel material. The surrounding gate structure ensures enhanced electrostatic control and increased sensitivity, crucial for detecting low-concentration biomolecules. The high carrier mobility of graphene minimizes short-channel effects and improves response times for real-time biosensing. Device simulations conducted using Silvaco TCAD reveal superior electrical performance, with an ION/IOFF ratio of 10⁷ and low ambipolar current, making the device highly energy-efficient. The reduced tunneling barrier width contributes to an impressive ON current of 10⁴ A/μm, enhancing signal detection for biomolecules in physiological samples. This work demonstrates the potential of InP/GaN-Graphene TFETs as highly sensitive biosensors for medical diagnostics and environmental monitoring applications.

In this article, we aim to put forward a simple, cost-effective, compact, and high-performance optical fiber biosensor for the detection of numerous cancer cells in the human body. The proposed sensor is grounded on a multimode optical fiber with an Au-coated U-shaped core. The designed optical fiber sensor (OFS) is modelled and analyzed exploiting finite element method (FEM)-based COMSOL Multiphysics. The working principle of the sensor is based on surface plasmon resonance (SPR) phenomenon excited on the surface of Au layer, which leads to generation of strong evanescent field near the sensing area. The backbone of this study is to investigate the coupling of plasmonic mode and core mode to get a maximum confinement loss, which intensifies the evanescent field near the sensing region. Numerous geometrical parameters such as thickness of Au layer, height of the channel, and width of the channel are carefully adjusted to accomplish optimum performance. The numerical simulation outcomes reveal that the proposed sensor can achieve a maximum sensitivity of 2352.94 nm/RIU, figure of merit of 24.15 1/RIU, and resolution of $4.3\times {10}^{-6}$ RIU. Above all, the proposed sensor is miniature in size, easy to fabricate, and facilitates real-time sensing of cancer cells, which can be a suitable candidate for bio-medical applications.