Albizia procera-Derived Nitrogen-Doped Carbon for Advanced Energy Conversion, Storage, and Environmental Technologies.

This review explores the diverse applications of nitrogen-doped carbon derived from Albizia procera, known as white siris. Native to the Indian subcontinent and tropical Asia, this species thrives in varied conditions, contributing to sustainable development. The nitrogen-rich leaves of Albizia procera are an excellent source for synthesizing nitrogen-doped carbon, which possesses remarkable properties for advanced technologies.

Deep regression analysis for enhanced thermal control in photovoltaic energy systems.

Efficient cooling systems are critical for maximizing the electrical efficiency of Photovoltaic (PV) solar panels. However, conventional temperature probes often fail to capture the spatial variability in thermal patterns across panels, impeding accurate assessment of cooling system performance. Existing methods for quantifying cooling efficiency lack precision, hindering the optimization of PV system maintenance and renewable energy output.

Predictive modeling and optimization of a waste cooking oil biodiesel/diesel powered CI engine: an RSM approach with central composite design.

Due to the restrictions of the diesel engine emissions and the massive demand of energy, the fossil diesel fuel has been consumed quickly and the resources cannot suffice the demand. Alternative fuels that include bio alcohols, hydrogen and biodiesel can make up the diesel fuel depletion. Biodiesel is convenient for diesel engine operation due to its properties like fossil diesel properties.

Neuroimaging Correlates of 3 Distinct Physical-Cognitive Phenotypes in Cognitively Normal Older Adults: The Gothenburg H70 Cohort Study.

Background And Objectives: Individuals aged 70 and older frequently experience an increased risk of deficits in both physical and cognitive functions. However, the natural progression and interrelationship of these deficits, as well as their neurologic correlates, remain unclear. We aimed to classify the data-driven physical-cognitive phenotypes and then investigate their associations with neuroimaging markers.