Chromium oxide nanoparticles as a modifier of carbon paste electrode for cyclic voltametric assessment of paracetamol in medicinal sample.

The development of novel electrochemical sensors has attracted significant interest in detecting pharmaceutical compounds with high sensitivity and selectivity. Chromium oxide nanoparticles (Cr₂O₃ NPs) are promising materials for modifying carbon paste electrodes (CPEs) due to their excellent electrochemical properties and conductivity. The CrO NPs were synthesized by the combustion method using, chromium (III) nitrate and glycine at 350 ℃.

Survey of healthcare-associated sink infrastructure, and sink trap antibiotic residues and biochemistry, in 29 UK hospitals.

Background: Hospital sinks are linked to healthcare-associated infections. Antibiotics and chemicals in sink traps can select for pathogens and antimicrobial resistance (AMR). Optimising sink design and usage can mitigate sink-to-patient dissemination of pathogens, but large-scale surveys of hospital sink infrastructure are lacking.

Molecular characterization of an endophytic strain of Bacillus subtilis with plant growth-promoting properties from a wild relative of papaya.

Aim: Bacillus subtilis is usually found in soil, and their biocontrol and plant growth-promoting capabilities are being explored more recently than ever. However, knowledge about metabolite production and genome composition of endophytic B. subtilis from seeds is limited.

A comprehensive review on early detection of drusen patterns in age-related macular degeneration using deep learning models.

Age-related Macular Degeneration (AMD) is a leading cause of visual impairment and blindness that affects the eye from the age of fifty-five and older. It impacts on the retina, the light-sensitive layer of the eye. In early AMD, yellowish deposits called drusen, form under the retina, which could result in distortion and gradual blurring of vision.

Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System.

The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS.