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The acid catalyzed reaction is one of the most important reactions in organic chemistry. Most acid catalyzed reactions have been studied in the presence of strong acids such as H2SO4, HNO3, HCl, BF3.O(Et)2, TFA, and methanesulfonic acid etc. in various organic solvents. The use of excessive and even stoichiometric amounts of acids with organic solvents raises environmental concerns. To avoid the use of corrosive acids and to reduce the environmental impact, the development of a new method for organic synthesis catalyzed by boric acid would be highly desirable. The benefits and uses of boric acid-catalyzed reactions were covered in this communication.

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Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.