Biosynthesized ZnO NPs loaded-electrospun PVA/sodium alginate/glycine nanofibers: synthesis, spinning optimization and antimicrobial activity evaluation.

In this work, microalgae-based zinc oxide nanoparticles loaded with electrospun polyvinyl alcohol (PVA)/sodium alginate (SA) nanofibers were fabricated by electro-spinner. PVA/SA fibrous mats were crosslinked by citric acid, which enhanced their thermal stability and swelling behavior. Green-synthesized ZnO NPs were laboratory synthesized and characterized by FTIR, XRD, EDX, SEM, TEM and TGA analyses.

Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement.

The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer.

Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory.

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases that influence its structure and operation. Such conditions may consist of coronary artery disease, which is characterized by the narrowing or clotting of the arteries that supply blood to the heart muscle, with the resulting threat of heart attacks. Heart rhythm disorders (arrhythmias), heart valve problems, congenital heart defects present at birth, and heart muscle disorders (cardiomyopathies) are other types of heart disease.

Enhanced human activity recognition in medical emergencies using a hybrid deep CNN and bi-directional LSTM model with wearable sensors.

Human activity recognition (HAR) is one of the most important segments of technology advancement in applications of smart devices, healthcare systems & fitness. HAR uses details from wearable sensors that capture the way human beings move or engage with their surrounding. Several researchers have thus presented different ways of modeling human motion, and some have been as follows: Many researchers have presented different methods of modeling human movements.

Impact of molecular configuration on the photoluminescence and electrical characteristics of poly-pyrrol-thiazol-imine polymers films.

The optical, photoluminescence, and electrical properties of Poly(Z)-PTI and Poly(E)-PTI, two Poly-Pyrrol-Thiazol-Imine polymers with comparable chemical structures but distinct configurations, were examined. Using the dip-casting method, polymer films were deposited on ITO substrates. UV-VIS spectroscopy revealed that both polymers diverged between 500 and 800 nm, showing the impact of molecular arrangement, but showed similar absorption behavior for wavelengths shorter than 500 nm.