Research

Nanotechnology has become increasingly important in the fabric industry due to its remarkable properties. One promising area of development is the production of medical textiles with antimicrobial properties. Nanotechnology allows for the creation of multifunctional textiles, including fabrics that are antibacterial, provide UV protection, are easy to clean, repel water and stains, and prevent odors. Researchers have been working extensively to improve substances and techniques that can effectively protect against various microorganisms. While chemical materials such as phenols, nitro compounds, and formaldehyde derivatives have been commonly used in the manufacturing of antibacterial medical textiles, their toxicity and poor biodegradability have limited their usage. To overcome these issues, the textile industry has turned to natural, non-toxic active substances that are safe for both people and the environment. 

Epilepsy is a well-known neurological disease caused by malfunctioning nerve activity in the brain. These malfunctioning causes episodes called seizures. Seizures in epileptic patients involve uncontrollable movements, loss of sensation, convulsions, and loss of consciousness, which can result in catastrophic injury and even death. Therefore, a computerized seizure recognition system is important to protect epilepsy patients from the risk of seizures. The main reason for this disorder is still unknown. Though the symptoms associated with seizures can be treated manually and the accuracy of the diagnosis depends on the experience of the technician. In this work, we presented an artificial intelligence-based approach where time–frequency characteristics of EEG signals are used to detect an epileptic seizure. Electroencephalography (EEG) is widely recognized for the diagnosis and evaluation of brain activity and disorders.

Approximately 68 percent of people around the world suffer from different levels of lactose intolerance. Previously some enzymatic lactose sensor was developed but they have some limitations such as high cost, low stability, low detection limit, etc. Recently, a nanoparticles-based sensor platform can detect electroactive species without the requirement of the enzyme. NiO nanoparticles have been found to have excellent catalytic activity toward carbohydrates. The structures and morphologies of the NiO nanoparticles are investigated using SEM and EDS. Cyclic voltammetry and amperometry measurements are used to study the electrochemical detection of lactose. The sensitivity of NiO/NF is 583 μA/mM/cm2, limit of detection is 40 M, linear behavior for lactose detection in concentrations up to 6 mM and response time is 12 s. The enhanced electron conductivity and many redox reaction sites on the surface of the sensor material contribute to the good electrochemical performances.

COVID-19 has become one of the most virulent, acute, and life threatening diseases in recent times. No clinically approved drug is available till now for its treatment. Therefore, early and swift detection is very essential for reducing overall mortality. The chest x-ray image is one of the possible alternative methods for detecting COVID-19. Researchers are exploring image processing techniques along with deep learning-based models like AlexNet, VGGNet, SqueezeNet, GoogleNet, etc. to detect COVID-19. This study aims to formulate, implement and investigate deep learning-based models and their probable hyperparameters tuning for obtaining the best results when identifying COVID-19 using chest x-ray images.