RESUMEN
Water scarcity has emerged as an intense global threat to humanity and needs prompt attention from the scientific community. Solar-driven interfacial evaporation and seawater desalination are promising strategies to resolve the primitive water shortage issue using renewable resources. However, the fragile solar thermal devices, complex fabricating techniques, and high cost greatly hinder extensive solar energy utilization in remote locations. Herein, we report the facile fabrication of a cost-effective solar-driven interfacial evaporator and seawater desalination system composed of carbon cloth (CC)-wrapped polyurethane foam (CC@PU). The developed solar evaporator had outstanding photo-thermal conversion efficiency (90%) with a high evaporation rate (1.71 kg m-2 h-1). The interfacial layer of black CC induced multiple incident rays on the surface allowing the excellent solar absorption (92%) and intensifying heat localization (67.37 °C) under 1 kW m-2 with spatially defined hydrophilicity to facilitate the easy vapor escape and validate the efficacious evaporation structure using extensive solar energy exploitation for practical application. More importantly, the long-term evaporation experiments with minimum discrepancy under seawater conditions endowed excellent mass change (15.24 kg m-2 in consecutive 8 h under 1 kW m-2 solar irradiations) and promoted its operational sustainability for multi-media rejection and self-dissolving potential (3.5 g NaCl rejected from CC@PU surface in 210 min). Hence, the low-cost and facile fabrication of CC@PU-based interfacial evaporation structure showcases the potential for enhanced solar-driven interfacial heat accumulation for freshwater production with simultaneous salt rejection.
RESUMEN
Chronic obstructive pulmonary disease (COPD) and pneumonia are two of the few fatal lung diseases which share common adventitious lung sounds. Diagnosing the disease from lung sound analysis to design a noninvasive technique for telemedicine is a challenging task. A novel framework is presented to perform a diagnosis of COPD and Pneumonia via application of the signal processing and machine learning approach. This model will help the pulmonologist to accurately detect disease A and B. COPD, normal and pneumonia lung sound (LS) data from the ICBHI respiratory database is used in this research. The performance analysis is evidence of the improved performance of the quadratic discriminate classifier with an accuracy of 99.70% on selected fused features after experimentation. The fusion of time domain, cepstral, and spectral features are employed. Feature selection for fusion is performed through the back-elimination method whereas empirical mode decomposition (EMD) and discrete wavelet transform (DWT)-based techniques are used to denoise and segment the pulmonic signal. Class imbalance is catered with the implementation of the adaptive synthetic (ADASYN) sampling technique.