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Solar desalination is a viable alternative for clean water for the areas where plenty of sunshine is available throughout the year. Due to less productivity of distilled water in passive solar still, it is still a challenge for the researchers to commercialize this low-cost renewable technology. In the last decade, much effort has been made by researchers to improve the overall productivity of solar stills by expanding novel active system methods. For the work presented here, a novel nanofluid-laden reverse-irradiated direct absorption solar collector (RI-DASC) augmented active solar still is proposed and tested under laboratory conditions for the very first time. Ag nanoparticles of sizes ~5-10 nm were used to prepare the nanofluids and were used as working fluid in the RI-DASC system. The system transfers the heat to the basin of active solar still directly omitting the need of piping arrangement. Experiments were conducted under different ambient conditions and for different mass loadings of Ag nanoparticles, that is, 0.1 mg L À1 , 0.2 mg L -1 , and 0.4 mg L À1 in RI-DASC augmented active solar still. The results showed that the temperature difference between the top glass cover and the basin of solar still was significantly higher in novel active solar compared to the conventional single slope passive still. Ag nanofluids with a mass fraction of 0.4 mg L À1 lead to a maximum temperature rise and thus overall thermal efficiency over other tested Ag nanofluids. The novel DASC augmented active solar still is practically viable using reflectors and can enhance the freshwater productivity over a conventional solar still.

Aspect category detection (ACD) is an important subtask of aspect‐based sentiment analysis (ABSA). It is a challenging problem due to subjectivity involved in categorization, as well as the existence of overlapping classes. Among various approaches that have been applied to ACD include rule‐based approaches along with other machine learning approaches, and most of them are statistical in nature. In this article, we have used an association rule‐based approach. To deal with the statistical limitation of association rules, we proposed a hybridized rule‐based approach that combines association rules with the semantic association. For semantic associations, we have used the notion of word‐embeddings. Experiments were performed on SemEval dataset, a standard benchmark dataset for aspect categorization in the restaurant domain. We observed that semantic associations can complement statistical association and improve the accuracy of classification. The proposed method performs better than several state‐of‐the‐art methods.

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