Optimization of energy management in Malaysian microgrids using fuzzy logic-based EMS scheduling controller.

The microgrid (MG) faces significant security issues due to the two-way power and information flow. Integrating an Energy Management System (EMS) to balance energy supply and demand in Malaysian microgrids, this study designs a Fuzzy Logic Controller (FLC) that considers intermittent renewable sources and fluctuating demand patterns. FLC offers a flexible solution to energy scheduling effectively assessed by MATLAB/Simulink simulations.

Comprehensive analysis and human health risk assessment of tap water quality in Dhaka City, Bangladesh: Integrating source identification, index-based evaluation, and heavy metal assessment.

Despite potential contamination, tap water remains the primary source of drinking in megacities. However, the sources of heavy metal(oid)s contamination and associated health hazards have not been thoroughly addressed in many developing cities, including Dhaka. Therefore, we made the first attempt to assess tap water quality in Dhaka City using indices, identify pollution sources with state-of-the-art techniques, and quantify associated health risks.

Unraveling Interleukin-1β inhibition: Computational insights into anti-inflammatory compound selection for inflammatory disorders.

The multifaceted impact of IL-1β has been proposed to have a central role in a spectrum of immunological responses spanning physiological reactions to aggressive inflammatory reactions and autoimmune disorders. Once IL-1β binds to its cognate receptor it initiates IL-1R1/TLR4 signaling cascade, leading to transcriptional modifications that sustain the inflammatory response. Extensive structural and functional investigations on IL-1β have yielded various inhibitors aimed at disrupting the formation of ligand receptor complex.

A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals.

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy.