A Condition Monitoring System for Electric Vehicle Batteries Based on a Convolutional Neural Network Using Thermal Image

A new monitoring technique has been developed to evaluate the capacity and performance of Lithium-ion batteries batteries by utilizing two convolutional neural networks (CNNs) models, Deep convolutional neural network (DnCNN) and CNN with BFGS quasi-Newton optimization. The system utilizes thermal images of lithium-ion batteries as input for training and testing. DnCNN model is utilised to accurately calculate battery capacity and performance, and the performance is evaluated using mean squared error (MSE) and PSNR. The CNN-based training method employs the BFGS quasi-Newton algorithm to measure battery capacity accurately by evaluating the mean squared error (MSE) and regression results. The proposed condition monitoring system using thermal imaging and CNN models, specifically the CNN- BFGS quasi-Newton algorithm model, can accurately detect battery capacity with an accuracy rate of 98.5%, compared to the DnCNN model with an accuracy rate of 96.7%. The proposed system can address the critical issue of battery capacity and degradation in EVs, providing a more sustainable and efficient alternative for real-time applications

PHYTOCHEMICAL SCREENING AND ANTIMICROBIAL ACTIVITY OF AZADIRACHTA INDICA AND PLECTRANTHUS AMBOINICUS EXTRACT

Objective: In the present research, a clear systematic investigation of phytochemical screening and antibacterial activity of herbal plants such as Azadirachta indica and Plectranthus amboinicus has been carried out. Methods: The aqueous and alcoholic extract was prepared in soxhlet apparatus and phytochemical analysis of extracts was performed and analysed. The in vitro antimicrobial activity was performed by cup plate method. These extracts were studied under agar diffusion method against three bacterial species such as Bacillussubtills, Staphylococcus aureus, and Escherichia coli at 5µg, 50 µg and 250 µg concentration. Results: The combine extract showed a predominant activity against these bacteria, which confirmed antimicrobial activity in AEAI and AEPA Conclusion: The results obtained in this study clearly indicate that AEAI and AEPA has a significant potential to use as an antimicrobial agen

Sequence-based identification of microbial contaminants in non-parenteral products

ABSTRACT Phenotypic profiles for microbial identification are unusual for rare, slow-growing and fastidious microorganisms. In the last decade, as a result of the widespread use of PCR and DNA sequencing, 16S rRNA sequencing has played a pivotal role in the accurate identification of microorganisms and the discovery of novel isolates in microbiology laboratories. The 16S rRNA region is universally distributed among microorganisms and is species-specific. Accordingly, the aim of our study was the genotypic identification of microorganisms isolated from non-parenteral pharmaceutical formulations. DNA was separated from five isolates obtained from the formulations. The target regions of the rRNA genes were amplified by PCR and sequenced using suitable primers. The sequence data were analyzed and aligned in the order of increasing genetic distance to relevant sequences against a library database to achieve an identity match. The DNA sequences of the phylogenetic tree results confirmed the identity of the isolates as Bacillus tequilensis, B. subtilis, Staphylococcus haemolyticus and B. amyloliqueficians. It can be concluded that 16S rRNA sequence-based identification reduces the time by circumventing biochemical tests and also increases specificity and accuracy