Electrical detection of biotic materials and their properties is always crucial in Biomedical Engineering. It emphasizes elaborate analysis of a certain disease diagnosis using electrical means which includes the study of electrical properties followed by detection, identification, and quantification of biotic entities. Proper and early detection of cells, which can be normal or cancerous, holds never-ending demand. This review emphasizes the electrical characterization methods in modeling and classification of cell properties. Moreover, standard methods have been highlighted and discussed to yield performance analysis. This study suggests that techniques presented for single-cell analysis (SCA) are always precise and hold great scope compared to those for cell mixture. SCA plays a promising role in disease diagnosis and treatment planning, as it not only detects the cells but also helps in identification. Distinct electrical-based cell detection techniques are highlighted, along with the pros and cons of various SCA devices. The content, in terms of methodologies and available techniques, of this review paper is useful to attract researchers working in this area.

COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes.

The present study explores the use of capacitance-voltage measurements to detect and estimate the number of total dissolved solids (TDS) in water suspensions. With the application of the electric field, the suspended solids get polarized. The generated polarization enables their electrical traits and affects the electrical field uniformity. This has been translated into a change in the suspension effective capacitance value. It has been observed that the effective-capacitance suspension increases with higher TDS concentrations. To electrically estimate the count of TDS in water suspensions, a calibration curve has been generated with the use of deionized water to de-embed the effect of the suspended media. The curve correlates the TDS count with the change in capacitance and has been used to construct a closed formula. The proposed approach has been validated using conventional meters. The estimated and measured concentrations corroborated well with each other. The capacitance-voltage method can detect and quantify lower and higher ranges than those of conductivity base meters.

As cyber-physical systems become increasingly virtualized, Digital Twins are emerging as critical components for real-time system monitoring, simulation, and control. However, their growing complexity and exposure to dynamic network environments render them susceptible to sophisticated cyber threats. Traditional cybersecurity models, often based on rules or based on machine learning, struggle to adapt in real time to evolving attack patterns, especially within decentralized and resourceconstrained settings. In this conceptual paper, we introduce the Neuromorphic Cyber-Twin (NCT), a brain-inspired architectural framework that leverages spiking neural networks (SNNs) and event-driven cognition to endow digital twins with adaptive, self-evolving cyber defense capabilities. The NCT framework is grounded in neuromorphic principles such as sparse coding, temporal encoding, and synaptic plasticity, e.g., Spike-Timing-Dependent Plasticity (STDP), enabling it to process telemetry data from the digital twin layer as spike-based sensory input. We present a layered architecture wherein the cognitive layer continuously monitors behavioral deviations, performs anomaly inference, and autonomously adapts its defense responses in alignment with system dynamics. This biologically inspired paradigm offers low-latency detection, contextual awareness, and energy efficiency, essential for scalable security in virtualized ecosystems. We discuss theoretical underpinnings, architectural components, and application scenarios across smart infrastructure, autonomous transport, and industrial control systems. The paper concludes with key research challenges and a roadmap for future implementation of neuromorphic cybersecurity within digital twin environments.