Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter.

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics.

Anaerobic co-digestion of oil-extracted spent coffee grounds with various wastes: Experimental and kinetic modeling studies.

The effect of oil extraction from spent coffee grounds as a pre-treatment strategy prior to anaerobic digestion besides assessing the feasibility of defatted spent coffee grounds co-digestion with spent tea waste, glycerin, and macroalgae were examined. Mesophilic BMP tests were performed using defatted spent coffee grounds alongside four co-substrates in the ratio of 25, 50, and 75%, respectively. The highest methane yield was obtained with the mono-digestion of defatted spent coffee grounds with 336 ± 7 mL CH/g VS and the yield increased with the increase in the mass ratio of defatted spent coffee grounds during co-digestion.

Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks.